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Why Junior Engineers Matter More as AI Expands

2026-05-27T00:00:00+00:00

The Adaptation of the Junior Engineer in an AI‑Accelerated Profession

The landscape has shifted. AI can generate code at a pace that would have been unthinkable a few years ago, but speed is not the work.

Speed cannot decide what should exist, why it matters, or whether it is safe. The belief that a junior can lean on AI and bypass the discipline is a misreading of the craft.

Early‑career engineers are needed more than ever because the judgement required to guide, verify, and constrain AI now sits at the centre of the role.

The junior position is not disappearing. It is being reshaped. AI has lowered the cost of producing code, but it has raised the cost of understanding what that code means. The work has not become smaller; it has become sharper, with an additional focus.

The organisations that recognise this early will keep their engineering discipline intact. The ones that do not will discover that AI exposes weaknesses in thinking faster than they can respond.

The Changing Weight of the Work

Typing has never been the job. It was simply the visible part of it. The real work — analysis, verification, risk thinking, system reasoning, and safety — has always carried the weight. AI accelerates the mechanical layer and exposes the cognitive one. Juniors now meet the deeper parts of the discipline sooner, and the expectations rise accordingly.

This shift is not cosmetic. It is economic. When code becomes cheap, correctness becomes expensive. The cost of a faulty assumption, a missed constraint, or a silent failure grows. The value of the junior engineer lies in their ability to prevent these errors before they harden into production.

AI Introduces New Types of Failure

When using an LLM in a pipeline, AI introduces new categories of failure: output-level instability, and behavioural-level instability.

Output-level Instability

LLMs are non-deterministic, probability machines.

Because of this schema drift, hallucinations, and silent truncation of results, can all ocur. The junior staff member will need to develop skills in detecting and handling all of these. These are changes in the way the LLM might respond to your system so your calling system must be robust to such variety.

Behavioural-level Instability

Across multiple LLM calls, even if the shape of the output result is the same, the behaviour of the LLM may change internally.

Given an identical prompt, "Extract the customer’s job title", and the same input, "My name is Helen and I work as a senior analyst at JPMG", the first call may return "senior analyst", the second may return "analyst", and the third may return "Senior Analyst".

In this case, all data passed to the LLM (the prompt and the input) and the output schema (a string in each case) remain the same. However, a change in the LLM’s internal behaviour has produced different outputs. Juniors need to be attuned to this possibility and know how to address it.

The Organisational Obligation

None of this works if organisations cling to the old model. Juniors cannot develop judgement in an old environment optimised for throughput. They need structured mentorship, slower reviews, and the psychological safety to test their reasoning.

Juniors need decision‑rights that are clear, not implied. Decision-rights are an understanding between the junior and their colleagues on what they can decide for themselves, and what they cannot and must seek input to resolve.

Juniors need leaders who understand that judgement is not taught by accident.

If the system does not adapt, the junior cannot.

Emerging Responsibilities

The adapted junior role becomes more investigative and more integrative. The work stretches across definition, verification, safety, and coherence.

Problem framing becomes central. Before any code is generated, the junior and their team must be clear on what the business is trying to achieve.
Constraint recognition grows in importance. Boundaries, risks, and compliance obligations must be surfaced early.
AI‑guided exploration replaces manual iteration. The junior evaluates options rather than producing them from scratch.
Verification discipline becomes essential. Plausible output is not enough. It must be correct, safe, and aligned with intent. AI can generate as much code as you want. But is it the right code? Determining whether generated code is the right code is part of the junior's role, supported by their team, the development process and wider engineering leadership.
Integration awareness develops sooner. Systems fail at the seams, not in isolation. The junior must develop skills to be aware of this and build solutions that are hardened to failure.
Operational literacy becomes expected. Logs, metrics, observability, and incident handling enter the junior toolkit.
Documentation clarity gains weight. Decisions must be legible and reproducible. "The AI did it" is not a defence.

Should your organisation invoke an LLM as part of a processing pipeline, token-level reasoning becomes a topic that needs addressing. Even with an identical prompt, an LLM's internal behaviour may vary unless steps are taken to constrain temperature, top-p, and top-k. However, even if these values are set to 0, 0, and 1 respectively (so that the LLM chooses the highest-probability next token), the quality of the response may decrease. This decrease is due to multiple factors: the LLM becoming overly literal when processing the prompt, and becoming less robust to ambiguous input. The LLM may fail on a task requiring synthesis or nuance as these require variety over the next token, not always the highest‑probability one.

These responsibilities demand human judgement. AI cannot supply it.

Failure‑Mode Literacy

Engineering maturity is measured by how you handle failure, not how quickly you produce output. Juniors must learn to read failure modes: what breaks, why it breaks, and how the system behaves under stress.

This is where judgement is forged.

Evaluating LLM output

Both output-level and behaviour-level instability require your junior to learn the discipline of evaluating model behaviour, not just observing it.

LLM output must be tested for schema reliability, instruction adherence, grounding fidelity, and deterministic stability. Behaviour must be measured over time so that drift is detected early rather than discovered in production.

Evaluation becomes part of the junior role because correctness is now the expensive part of the work. AI accelerates your ability to produce code, so humans must strengthen verification.

Juniors often see AI‑generated artefacts first, which means they become the first line of defence against drift, hallucination, and structural failure.

The junior role is not shrinking, it is moving closer to the centre of the system.

Schema reliability

Schema reliability is the stability of the output structure across calls. It asks whether the model returns the same shape every time. A reliable schema preserves field names, nesting, ordering, and types. When schema reliability is weak, downstream systems break: parsers fail, validators reject output, and silent truncation corrupts results. Juniors must learn to detect when the structure shifts, even subtly, because structural instability will cause production failure.

Instruction adherence

Instruction adherence is the model’s ability to follow the constraints it was given. It measures whether the output respects required fields, forbidden content, formatting expectations, safety constraints, and domain‑specific rules. Weak adherence produces plausible but incorrect output that appears compliant but violates intent. Juniors must learn to test adherence explicitly, because LLMs often drift away from constraints under load, ambiguity, or long contexts.

Grounding fidelity

Grounding fidelity is the degree to which the model’s output remains anchored to the provided context, data, or retrieval results. High fidelity means the model stays within the evidence; low fidelity means it fabricates, embellishes, or substitutes. This is the core defence against hallucination. Juniors must learn to check whether each claim in the output can be traced back to a source. Without grounding fidelity, correctness becomes guesswork and organisational risk increases.

Deterministic stability

Deterministic stability is the consistency of the model’s behaviour under identical conditions. It measures whether repeated calls with the same prompt, same context, and same parameters produce meaningfully similar results. Instability here signals deeper behavioural drift: model updates, sampling variance, context‑window rollover, or upstream nondeterminism. Juniors must learn to monitor this stability because unpredictable behaviour, even within a fixed schema, undermines trust in the system.

Once evaluation becomes routine, the next layer of responsibility emerges. Understanding how AI‑driven behaviour interacts with organisational risk, regulation, and safety boundaries becomes a concern.

Compliance and Safety

AI introduces new liabilities. Licensing, data handling, regulatory expectations, model hallucinations, and architecture all sit inside the junior’s world now. The business must help them to learn to recognise unsafe output and understand the organisational risk attached to it. Secure by default is no longer a slogan; it is a habit.

Once an LLM becomes part of your production pipeline, it represents a system-level reliability concern. Junior colleagues will need to understand retrieval hops, orchestration cost, and architectural latency.

Creation vs Integration

Many teams still confuse "using a chatbot to generate new code" with "running an LLM inside a production pipeline". These are not the same problem: the former accelerates creation, while the latter introduces system‑level reliability concerns that juniors must learn to evaluate.

But even chatbot‑generated code is not free. It must still be evaluated to answer the question: "is adding this code into our system the right thing to do?"

The distinction matters because both activities demand judgement, but pipeline integration demands system‑level reasoning and reliability awareness.

The Apprenticeship Model Returns

AI compresses the early stages of skill acquisition because the novice to intermediate gap is mostly about knowledge access, pattern exposure, and basic scaffolding.

A novice must learn vocabulary, syntax, idioms, and the shape of common solutions ("house rules"). An LLM can supply this information instantly: it provides examples, explanations, and templates on demand. This removes much of the friction that traditionally slows early progress, so with AI the distance between novice and intermediate shrinks.

But the intermediate to senior gap is not reduced, because seniority is not a knowledge problem. It is a judgement problem formed through apprenticeship: pairing, review, reflection, and exposure to real events on real systems under real constraints.

Senior engineers develop taste, trade‑off literacy, failure intuition, and a sense of responsibility for long‑term consequences. These abilities cannot be acquired through text prediction alone. They come from lived experience with real systems, real failures, and real organisational pressures.

AI accelerates learning, but senior judgement is produced by responsibility, constraint, and lived experience. These are conditions that AI cannot inhabit. The craft remains intact because the essence of mastery is grounded in practice shaped by real systems, real failures, and real organisational pressures, not by information alone.

Juniors must learn the difference between additive work (generating new code), and transformative work (modifying existing systems). To transform an existing system safely requires judgement. Your organisation will need to support your junior colleague in developing that judgement given your company's unique codebase, infrastructure and culture.

A New Path to Seniority

Seniority emerges from judgement, not keystrokes. The route to senior for the junior shifts toward structure, risk, and operational thinking.

Architecture literacy develops earlier. Patterns and constraints become part of daily reasoning.
Risk thinking becomes foundational. Engineers learn to anticipate failure and design for recovery.
Review competence shifts from syntax to structure. The question becomes: does this code make sense?
Operational competence becomes core. Observability and incident handling help to shape judgement.
Decision clarity becomes a differentiator. Seniors articulate reasoning, not just outcomes.
Cross‑functional communication grows in importance. Complexity must be translated into clarity.

Juniors are ideally placed to contribute to AI-augmented team processes: reviewing AI-generated artefacts, maintaining team-level shared understanding, and helping to ensure coherence across accelerated workflows.

The work becomes less about producing code and more about shaping the conditions in which code can be trusted.

The Cultural Shift

High‑pace environments often reward noise. AI accelerates that tendency. But the teams that thrive will be the ones that reward clarity instead. Juniors need a culture that values slow thinking at the right moments, not constant motion.

Expectations of juniors will vary depending on the AI‑maturity of your organisation.

In low‑maturity environments, juniors are forced to compensate for weak processes, unclear decision‑rights, and inconsistent use of AI.

In high‑maturity environments, juniors grow faster because the system around them is stable: prompts are versioned, retrieval is predictable, evaluation is routine, and model updates are treated as engineering events. The culture determines whether AI becomes an accelerant for judgement or a multiplier of confusion.

Practical First Steps for Juniors

Learn to articulate intent before touching a tool.
Practise verifying AI output with suspicion and skepticism, not trust.
Build small systems and observe how they behave under load.
Document decisions as if someone else must rely on them.
Study failure modes; they teach more than success ever will.

Practical First Steps for Leaders

Define decision‑rights explicitly. What can a junior decide for themself?
Slow down reviews to create space for reasoning.
Pair juniors with seniors intentionally, not incidentally.
Treat AI as an accelerator, but only within well‑understood and defined boundaries.
Build a culture where clarity is rewarded and noise is not.

AI is a tool. How can you best use that tool to help the junior do their best work? AI is not a replacement for the junior but an assistant.

The Evolving Value of the Junior Engineer

Juniors become force multipliers. They use AI to explore the solution space, stress‑test assumptions, and verify generated artefacts. They learn system thinking earlier and contribute meaningfully sooner. But only if the organisation supports them.

Ask not what your junior can do for you — ask what you can do for your junior.

Final Thoughts

Engineering is not being erased. It is being reweighted. Humans decide what should exist, why it matters, and whether it is safe. AI writes the code. The profession continues to evolve, but its centre of gravity remains the same: judgement, clarity, and the ability to read systems before safely changing them.

Agents Cannot Maintain Systems: The Additive–Transformative Gap in LLM Software Delivery

2026-05-21T00:00:00+00:00

This article explains why current LLMs cannot safely modify real software systems, despite impressive code‑generation demos.

Table of contents

The Promise of Automated Software Delivery

In 2026, the automated software delivery dream is for an agent to:

read a repository
understand project structure
plan a multi‑step change
write code, tests, and docs
run the code and fix its own mistakes
produce a PR‑ready diff

The first three tasks are additive; the last three are transformative. The first three add information without changing the behaviour of the system: they require reading, mapping, and planning, but not altering any existing causal structure in the codebase.

Applying new code is self-contained, additive work; modifying an existing system is transformative work that requires an understanding of dependencies, invariants, and consequences. This distinction — additive vs transformative — is the core reason current LLMs can assist but cannot autonomously deliver software.

Parts of the above can be done but only for tightly controlled demos on simple code that is tens of lines long, not on real-world repositories with thousands of lines of code that has existed for years where dozens of people have updated it.

What the Labs Have Actually Delivered

The agentic work of OpenAI, Google, Cognition Labs, GitHub (Microsoft), Sourcegraph, JetBrains, Replit, Amazon, Meta, and Anthropic, that is listed in Further Reading, was published in 2023 and 2024.

Depending on where you look, you may have been given another impression: that "agents are here". However, reality tells a different story.

Agents are improving, but are not reliable, not autonomous, and not production‑safe.

LLMs can assist with software delivery, but they cannot own it.

Why is this?

LLMs generate statistically plausible continuations of text. This works well for self-contained tasks like writing a function or drafting documentation because these are pattern‑extension problems. But pattern‑matching is not system understanding, and plausibility is not correctness.

Software systems are causal: components depend on each other, invariants constrain behaviour, and changes propagate through the system. The moment a task stops being self‑contained and becomes system‑dependent — requiring dependency coherence, persistent state, or awareness of how changes ripple through a real codebase — pattern‑matching is no longer sufficient.

Currently, LLMs can imitate the shape of engineering work, but they cannot maintain a stable internal representation of a system that must be coherently changed, and that gap is exactly why LLMs fail the moment the task becomes system‑level.

Persistent state creates temporal dependencies

A self‑contained task has no past and no future. A system‑dependent task does.

As soon as a change depends on:

previous writes
accumulated data
cached values
long‑lived objects
external system state

any agentic model must reason about how the system got here and how it will behave after the change.

LLMs cannot maintain that internal causal chain.

Writing code to Agentic Systems: The Fundamental Gap

The gap becomes clear when you compare two activities: writing new code and modifying an existing system.

Code generation is local and additive: the model extends a pattern without needing to understand the system.

But agentic work is global and transformative: the LLM must change the system itself, which requires understanding dependencies, invariants, interactions, and downstream consequences.

This is causal reasoning, not pattern extension. LLMs predict tokens, not consequences — and that is why the leap from writing code to producing a safe, system‑aware PR‑ready diff is not incremental but a shift into a fundamentally different problem space.

Producing a PR‑ready diff (the section in question)

A pull request (PR) is a piece of code that will change a system.

For that change to be safe, the change must respect the system's current architecture, its intent, and all downstream consequences.

Software engineers work hard to ensure that such a change is safe through testing and their own judgement and experience before having a collegue review the change.

Applying a change is no longer pattern-matching but understanding causal behaviour: how will the system change if this PR is applied?

The correctness of the PR depends on understanding the whole system, not just generating text.

The LLM must change the system, which requires understanding dependencies, invariants, interactions and consequences, all of which demand causal reasoning, not pattern matching.

Pattern‑matching can write code; only causal reasoning can maintain systems.

What can I do?

Confirm for yourself any claim that you see. Define your own realistic real-world repository to work on, one that is thousands of lines of code, that has supported past real-world work patterns.

Having your own results, applied to your own repository will tell you volumes more than any press release or online anecdote.

For the moment:

treat agentic AI as a strategic direction
treat current tools as assistants, not engineers
invest in clarity, architecture, and test discipline
expect progress, but not miracles
do not plan delivery pipelines around unproven capabilities

Maintain human judgement as the centre of the system.

The dream is intact. The evidence is not yet here.

Why this matters: code is cheap, judgement is not

LLM-augmented software delivery does not remove engineering.

It moves engineering up a level.

Humans need to focus on:

intent
constraints
architecture
correctness
safety
trade‑offs

The desired end state is not "AI writes code" but AI maintains systems. If we get there, humans will still need to maintain intent.

The consequence of an agentic system is not to remove engineering, but to elevate it, so that teams spend less time on mechanical construction and more time on judgement, alignment, and shaping the environment in which agents operate.

The organisations that benefit most will be those that treat agentic development not as automation, but as a structural shift in how software is conceived, validated, and maintained.

Final Thought

Until AI can reason causally about systems, human judgement remains the foundation of software delivery.

The Promise of Automated Software Delivery
What the Labs Have Actually Delivered
Why is this?
Persistent state creates temporal dependencies
Writing code to Agentic Systems: The Fundamental Gap
Producing a PR‑ready diff (the section in question)
What can I do?
Why this matters: code is cheap, judgement is not
Final Thought
Related Work
Table of Contents
Further Reading

Team-Based AI Engineering is Next Step After Individual AI for Coding

2026-05-05T00:00:00+00:00

Table of contents

Modern software teams are already moving faster because individual engineers use AI. Yet the real gains are still ahead. The biggest improvements do not come from speeding up coding. They come from speeding up the work that happens between people. That is where most of the time is lost, and where AI has the greatest leverage when applied at the level of the team.

A software engineer using AI increases their coding speed by 30 to 75 percent. But coding is only 30 percent of the job. The remaining 70 percent is the work that makes coding possible, safe, and correct. This work is shared, and it is deeply tied to the rest of the team.

Requirements, clarification and planning (15 to 20 percent)
Meetings and coordination (10 to 15 percent)
Code review (10 to 15 percent)
Debugging, testing, and validation (15 to 20 percent)
DevOps, tooling, and environment work (5 to 10 percent)
Documentation and knowledge work (5 to 10 percent)

These figures come from McKinsey, GitHub, Stripe, and Harris Poll. They show that most of an engineer’s time is spent on team‑level activities.

Modern Software is delivered by Teams

These twelve activities shape team throughput. Every delivery team performs them, and they determine how quickly and safely software moves from idea to production.

Task	Activities	Purpose
1. Understand and Shape Work	- Product discovery - Prioritisation - Requirements shaping - Trade off decisions - Roadmapping - Forecasting	This is where the team decides what to build and why.
2. Plan and Coordinate Delivery	- Sprint planning - Iteration planning - Capacity planning - Cross team alignment - Risk identification - Risk mitigation	This is the team level coordination layer.
3. Design the Solution	- Architecture design - System design - API design - Interface design - Technical decisions - Design documentation	This is where the team decides how to build it.
4. Build the Solution	- Coding - Test creation - Refactoring - Local environment work	This is the implementation phase.
5. Validate and Integrate	- Code reviews - Automated testing - Manual testing - Integration workflows - Merge workflows	This is the quality and integration gate.
6. Iterate and Fix	- Debugging - Fixing test failures - Addressing review comments - Retesting	This is the iteration loop.
7. Deploy and Operate	- Release management - Monitoring - Observability - Incident response - On call operations	This is the operational responsibility layer.
8. Learn and Improve	- Retrospectives - Post incident reviews - Process improvement - Tooling upgrades	This is how the team improves its delivery system.
9. Maintain Flow	- Manage work in progress - Unblock teammates - Reduce handoff delays - Remove bottlenecks	This is the team’s ability to maintain throughput.
10. Manage Team Knowledge	- Documentation - Architecture knowledge - Domain knowledge - Onboarding new engineers	This is the team’s collective memory.
11. Communicate and Align	- Stakeholder updates - Status reports - Cross team communication - Decision logging	This is the communication layer that keeps the system coherent.
12. Govern and Ensure Compliance	- Security reviews - Regulatory compliance - Data governance - Risk management	This is essential in regulated, cloud native environments.

These twelve activities define how modern software is delivered. Every engineer contributes to them, but not in equal measure. To understand where AI creates leverage, we need to look at how an engineer’s time maps onto this system. That is what the next section describes.

What an Engineer Does

The work of an engineer is given in the Engineer Time column, their work feeding into the team activities described in column two.

Engineer Time	Team Activities	Why this is Necessary
Requirements, clarification, planning	1. Understand and Shape Work; 2. Plan and Coordinate; 3. Design the Solution; 11. Communicate and Align	Engineers must understand the problem, shape requirements, and make trade offs before design.
Meetings and coordination	2. Plan and Coordinate; 9. Maintain Flow; 11. Communicate and Align; 12. Govern and Ensure Compliance	Coordination keeps work flowing, dependencies managed, and compliance aligned.
Coding	4. Build the Solution	Engineers turn all the work thus far into working computer code, using business infrastructure, processes and standards.
Code review	5. Validate and Integrate; 6. Iterate and Fix; 10. Manage Team Knowledge	Code review is the quality gate, integration control point, and knowledge sharing mechanism.
Debugging, testing, validation	4. Build the Solution; 5. Validate and Integrate; 6. Iterate and Fix; 7. Deploy and Operate	Debugging and validation dominate the iteration loop and ensure correctness end to end.
DevOps, tooling, environment work	4. Build the Solution; 7. Deploy and Operate; 8. Learn and Improve; 9. Maintain Flow	Tooling and environment work underpin build stability, deployment reliability, and flow.
Documentation and knowledge work	1. Understand and Shape Work; 3. Design the Solution; 10. Manage Team Knowledge; 11. Communicate and Align	Documentation is the team’s shared memory and design clarity mechanism.

The two hghlighted rows show the "coding" step, that is predominantly done by the software engineer alone.

Coding is the final expression of a much larger collaborative effort. The other 70 percent of the role ensures that what is coded is the right thing, built the right way, that is safe to run in production.

Software Engineer Adoption of AI is Individual

Developers are adopting AI tools on their own, at scale, and ahead of their organisations. JetBrains reports that 90 percent of developers now use at least one AI tool at work, and 74 percent have adopted specialised assistants independently. GitHub finds the same pattern: engineers use AI to improve their own speed and reduce cognitive load, not to change team workflows.

The result is a widening gap between personal productivity and the unchanged delivery system that the individuals operate within.

Accelerate One, Accelerate Many

When AI speeds up one engineer, it speeds up the interactions around them: reviews, iteration loops, testing throughput, coordination, and decision making. These effects compound across the delivery system.

Yet individual AI only improves the local interactions that depend on that engineer. Team level AI improves the global interactions that depend on shared context, shared artefacts, and shared decision making.

A team benefits from individual uplift, but several categories of work cannot be improved by individual tools alone.

Section Title	Activities	Summary
Individual AI cannot see or manage the team’s shared context	An engineer’s AI assistant only sees: - the engineer’s code - the engineer’s tasks - the engineer’s local context It cannot see: - the team’s backlog - the team’s dependencies - the team’s decisions - the team’s risks - the team’s architecture - the team’s workflow state Without this shared view, individual AI cannot improve: - planning - coordination - cross team alignment - decision logging - risk management	These are team level responsibilities, and they remain untouched.
Individual AI cannot improve the quality of shared artefacts	Even if every engineer uses AI, the team still has: - unclear requirements - inconsistent designs - missing decision records - uneven documentation - fragmented knowledge A team level AI can: - rewrite requirements for clarity - detect ambiguity across stories - maintain design consistency - summarise decisions - keep documentation aligned	This is a different category of improvement.
Individual AI cannot reduce waiting time between roles	Most delays in delivery come from: - waiting for a review - waiting for clarification - waiting for a decision - waiting for a fix - waiting for alignment A team level AI can: - answer clarifying questions - surface missing information - propose decisions - highlight blockers - keep flow moving	This is where the real throughput gains lie.
Individual AI cannot coordinate across roles	A delivery team includes: - product - design - QA - DevOps - security - architecture A team level AI can: - translate between roles - maintain shared understanding - track dependencies - keep everyone aligned	This is essential for predictable delivery.
Individual uplift is local; team uplift is structural	Individual AI improves: - how fast a person works Team level AI improves: - how the team works The first is additive. The second is multiplicative.	Team‑level improvements are multiplicative because they affect several people across the team’s communication network, not just the individual who uses the tool.

A team cannot reach the next level of performance without AI that operates on the shared system, not just the individuals within it.

When every member of the delivery team becomes faster and clearer in their part of the system, the throughput of the whole team increases non linearly.

Team Throughput

Team throughput is shaped by the slowest interaction in the workflow. Delivery moves when shared activities move: reviews, fixes, integration, decisions, documentation, coordination, and onboarding.

Onboarding shows this clearly. A new engineer becomes productive when they understand the system, the domain, the architecture, the conventions, and the team’s way of working. These are team level artefacts. AI helps only when the team applies it to the shared knowledge and processes that support this learning.

AI Acceleration

AI can speed up every shared activity listed above. These activities are constraints that the whole team depends on. When they move, the system moves. The effect is non linear because software delivery is dominated by interaction rather than individual effort.

Faster reviews, clearer decisions, and quicker coordination reduce the waiting time between people, which shortens the entire cycle.

Example: How reduced waiting shortens the cycle

Imagine a team working on a small feature. The work passes through five steps:

Write the change
Wait for review
Apply fixes
Wait for approval
Merge and test

Without team level AI

Writing the change: 3 hours
Waiting for review: 1 day
Fixing comments: 1 hour
Waiting for approval: half a day
Merging and testing: 2 hours

The total time is not the 6 hours of work. It is the 1.5 days of waiting wrapped around it.

Team level AI reduces waiting

Team level AI helps the reviewer by summarising the change, checking for risks, and drafting comments. It helps the author by preparing fixes and clarifications, and by coordinating activity through the five stages.

The waiting times drop:

Writing the change: 3 hours
Waiting for review: 2 hours
Fixing comments: 30 minutes
Waiting for approval: 1 hour
Merging and testing: 2 hours

The work is still roughly 6 hours, but the waiting has fallen from 1.5 days to about 5 hours. With an 8 hour day, the cycle drops from 18 hours to 11.

Reducing idle time is key

The work has not changed. The gain comes from removing the idle time between people. Reducing waiting shortens the whole cycle. This is where team level AI has its strongest effect. It acts on the delays that dominate delivery, not the small pockets of individual effort.

When these delays shrink, the system moves more quickly. Reviews happen sooner, decisions are clearer, fixes flow more easily, and work spends less time sitting in queues. The improvements are non linear because the team is no longer held back by the slowest interaction.

AI Benefits at the Team Level

The gains that matter most cannot be achieved through individual AI use alone. Individual uplift improves personal speed, but it does not change the structure of the team’s workflow or the quality of the shared artefacts that the team relies on.

Team level performance improves only when AI is applied directly to the collective work: shaping requirements, coordinating plans, reviewing code, integrating changes, resolving ambiguity, documenting decisions, and keeping flow steady.

These activities form the delivery system. Improving them requires AI that operates at the level of the team rather than the individual.

Why Team AI is Necessary

Individual uplift improves the outputs that flow into team interactions. It does not improve the interactions themselves. The main bottlenecks in delivery are the points where people must work together: clarifying requirements, resolving ambiguity, negotiating trade offs, coordinating across roles, and maintaining shared understanding.

Individual AI helps a person contribute more quickly. Team level AI improves the clarity, accuracy, and speed of the shared work that binds the team together. This is where the real gains lie.

Team level AI

A team level AI agent can work on the shared system:

rewrite requirements for clarity
maintain architecture knowledge
surface risks
detect ambiguity
summarise decisions
generate consistent patterns
keep the team aligned
handle coordination and scheduling

Individual AI cannot do this because it has no view of the team’s shared context.

Individual AI cannot coordinate across roles

A delivery team includes product, design, QA, DevOps, security, architecture, and delivery management. Each role uses different tools and produces different artefacts. Individual AI tools do not coordinate across these boundaries.

A team level AI agent can maintain shared context, track dependencies, surface risks, ensure consistency, support the Agile process, and reduce coordination friction.

Team level uplift is a multiplier

Individual uplift is additive. It makes each person faster, but it does not change the structure of the system. Team level uplift is multiplicative. It changes the structure of the system, reduces shared constraints, collapses waiting time, improves flow, and increases throughput across the whole team.

This is why team level AI is required to unlock the full return on investment.

Conclusion

The shift to AI in software engineering will not be won through individual adoption alone. Teams already feel the lift from faster coding and quicker local tasks, but the real gains come when AI is applied to the shared work that governs how delivery actually happens. The constraints that slow teams down are collective, and so the improvements that matter must be collective as well.

The organisations that move first will be the ones that treat AI as part of their delivery system, not as a personal tool. They will use it to keep work flowing, reduce waiting, maintain shared understanding, and support the decisions that shape the product. Once AI is embedded at this level, the team’s throughput changes in a way that individual uplift can never reach.

The opportunity is simple. Teams that adopt AI together will outpace those that adopt it alone. The sooner a team treats AI as part of its operating model, the sooner it sees the return that individual tools cannot deliver.

Modern Software is delivered by Teams
What an Engineer Does
Software Engineer Adoption of AI is Individual
Accelerate One, Accelerate Many
Team Throughput
AI Acceleration
- Example: How reduced waiting shortens the cycle
AI Benefits at the Team Level
Why Team AI is Necessary
Team level AI
Individual AI cannot coordinate across roles
Team level uplift is a multiplier
Conclusion
Related Work
Table of Contents
Further Reading

Global AI Trends 2024–2025

2026-05-04T00:00:00+00:00

Table of contents

Global Trends in AI

Artificial intelligence has entered a new phase. It is no longer a pilot or proof of concept. AI is core infrastructure; a technology that shapes how economies operate and how firms compete.

Evidence from the Microsoft AI Economy Institute (AIEI), Stanford HAI, and McKinsey shows rapid adoption and a widening gap between leaders and others. What follows is a concise summary of the period from 2024 to 2025, based solely on verified and reliable evidence.

The global evidence shows fast adoption, rising capability, and a widening gap between regions. These patterns set the context for the country level picture, where the United States remains a major driver of development, investment, and commercial uptake.

Global picture

Global adoption and diffusion

The AIEI reports that roughly one in six people worldwide used a generative AI tool in the second half of 2025. The same study states that 24.7 percent of the working age population in the Global North used generative AI tools, compared with 14.1 percent in the Global South. The AIEI attributes this gap to differences in infrastructure, skills, and policy readiness.

Commercial traction and investment

The State of AI Report 2025 notes that 44 percent of United States businesses paid for AI tools in 2025, up from 5 percent in 2023. UNCTAD in its 2023 Technology and Innovation Report confirms strong global growth in AI related companies and investment, especially in economies with established technology sectors and supportive policy environments.

Conclusions

The global evidence points to three clear conclusions.

First, AI use is now widespread. McKinsey reports that 88 percent of firms use AI in at least one function, though most have yet to scale it across the enterprise.

Second, capability continues to rise. Stanford HAI shows sharp year‑on‑year improvements in benchmark performance and a steep fall in model‑usage costs.

Third, investment is concentrated. The United States leads private AI investment, with China closing the performance gap in model quality.

In the Future

The verified evidence suggests three grounded developments.

First, wider business uptake is likely. McKinsey finds most organisations are still in pilot mode, implying further diffusion as workflows are redesigned.

Second, capability gaps between regions may widen. The AIEI reports higher adoption in the Global North, driven by infrastructure and skills, and Stanford HAI shows the United States and China pulling ahead in model development.

Third, investment patterns point to continued commercialisation. Stanford HAI records strong private investment in generative AI, with the United States far ahead of other economies.

These trends indicate a maturing technology, uneven readiness across regions, and a period where firms that can integrate AI into workflows will move faster than those still experimenting.

North America

United States

The State of AI Report 2025 reports that United States organisations continue to lead in frontier model (LLM) development and commercialisation. The AIEI diffusion study places the United States 24th globally for working age usage of generative AI tools, at 28.3 percent. The Federal Reserve Board in its 2026 FEDS Note reports high AI adoption in United States professional services and financial services.

Canada and Mexico

Statistics Canada reports that 12.2 percent of Canadian firms used AI to produce goods or deliver services in 2025, with a further 14.5 percent planning to adopt AI within the following year.

This reflects a steady rise in enterprise use rather than a population level diffusion measure.

Broader policy material, including the Pan Canadian Artificial Intelligence Strategy and the work of institutes such as Amii, Mila, and Vector, confirms an active national ecosystem but does not provide quantified adoption metrics.

Mexico

The OECD reports that around 20 percent of Mexican firms use at least one AI technology, but this is a general AI adoption figure, not a generative AI diffusion metric and is not tied to 2024 to 2025 specifically.

Conclusions

The United States stands out for commercial uptake. In the U.S., public uptake is clearly more advanced, with clearer evidence of scale and investment.

Canada’s AI uptake is driven mainly by firms rather than the general population. The Statistics Canada figures point to a measured, incremental pattern of adoption, with a clear pipeline of organisations preparing to introduce AI into their operations. The wider national ecosystem is active, but the absence of quantified diffusion data means the scale of use beyond the enterprise level cannot be assessed.

Mexico’s position is different. The OECD figure shows that a notable share of firms use at least one AI technology, but the measure is broad and not tied to generative AI or the 2024–2025 period. The available evidence therefore gives a sense of adoption but not its depth, maturity, or rate of change.

Looking to the Future

Canada and Mexico

The verified material suggests that Canada’s enterprise‑level adoption is likely to continue rising, given the proportion of firms planning to adopt AI and the presence of established research institutes. The lack of population‑level data remains a gap, limiting visibility of wider diffusion.

Mexico’s general adoption figure indicates that AI is present across parts of the economy, but the absence of more granular or time‑specific data makes it hard to track progress or compare with other regions. Both countries would benefit from more consistent measurement to understand how adoption evolves over time.

The United States

The United States shows a more advanced stage of AI commercialisation than its neighbours. The scale of paid use indicates that AI has moved beyond trial activity and is now embedded in day‑to‑day business operations. This reflects a market where firms are not only experimenting but committing resources and integrating AI into core workflows.

The strength of the U.S. research and investment base reinforces this position. A large share of global private investment, combined with a concentration of leading model developers, gives the U.S. a structural advantage. This creates a feedback loop: strong domestic capability supports commercial uptake, and commercial uptake in turn drives further capability.

Public use also appears more developed. Higher adoption levels across the Global North, combined with the U.S. role as a major producer and buyer of AI systems, point to a broader diffusion of tools into everyday work and consumer contexts.

Taken together, the evidence shows an economy where AI is already part of the operational fabric, supported by deep investment, strong research output, and a business environment that moves quickly from experimentation to deployment.

How U.S. businesses can build on their current position

The evidence shows that the United States holds two structural advantages: strong commercial uptake and deep private investment. China, by contrast, leads in large‑scale deployment in specific sectors and in state‑directed industrial programmes. These differences shape how firms in each country can move.

For U.S. businesses, the main advantage is speed. The high rate of paid use means firms are already integrating AI into everyday operations. This allows them to refine workflows, build internal capability, and compound gains earlier than competitors. The depth of private investment also gives U.S. firms access to a broad supply of models, tooling, and infrastructure, which lowers the cost of experimentation and adoption.

China’s strength lies in coordinated deployment across priority sectors. This creates scale quickly, but it also means firms operate within a more directed innovation environment. U.S. firms, by contrast, benefit from a more open commercial ecosystem, where competition between providers drives rapid improvement in tools and services.

The practical insight is that U.S. businesses can move faster because the commercial environment rewards early adoption and continuous iteration. They can integrate AI into products and operations without waiting for sector‑level programmes or central coordination. This gives them room to differentiate on execution, workflow design, and customer experience.

In short, the U.S. position allows firms to take advantage of a mature market, strong investment flows, and a competitive supply base, while China’s model favours rapid scaling within targeted sectors. Each system has its strengths, but the U.S. environment gives individual firms more freedom to act and adapt.

Europe, Middle East and Africa

Europe

Euronews in 2026, reporting on Eurostat generative AI usage data, identifies Norway, Ireland, France, and Spain as leaders in individual level adoption. Euronews also reports that countries with strong digital infrastructure, sustained skills investment, and mature employer practices show the highest usage. The same reporting highlights Europe as an active digital governance environment, although specific AI laws are not detailed in the confirmed sources.

United Kingdom

The United Kingdom appears consistently in major global analyses as a leading centre for AI research, policy development, and commercial activity.

The State of AI Report 2025 highlights the United Kingdom's role in research of frontier models (LLMs) and safety research. UNCTAD in its 2023 Technology and Innovation Report places the United Kingdom among economies with strong technology sectors and supportive policy environments.

Middle East

The AIEI diffusion study identifies the United Arab Emirates as the leading country per capita globally for working age usage of generative AI tools, at 64.0 percent in late 2025. The same study places Singapore second globally at 60.9 percent. The AIEI attributes these results to early investment in infrastructure, skills, and government adoption.

Africa

The AIEI diffusion study reports that AI adoption in the Global North has grown nearly twice as fast as in the Global South. Africa is considered part of the Global South. The AIEI attributes lower adoption in the Global South to differences in infrastructure, skills, and policy readiness.

Conclusions

The direction of travel across Europe, the Middle East, and Africa differs markedly from the paths taken in the United States and China. Europe’s leading adopters show a pattern built on long‑term institutional strength: digital infrastructure, skills pipelines, and employer practices that support steady, broad‑based uptake. This creates a slower but more stable trajectory, shaped by governance and capability rather than market speed.

The United Kingdom follows a related but distinct route. Its position is driven by research depth, frontier model work, and policy activity. This gives the UK influence in shaping standards and governance, even if its commercial scale is smaller than that of the United States.

The Middle East, led by the UAE, shows a different model again. High usage levels reflect rapid state‑led investment and fast public‑sector adoption. This is a top‑down route to diffusion, where national strategy translates quickly into workforce behaviour.

Africa’s position reflects structural constraints. Lower adoption is tied to infrastructure, skills, and policy readiness. The pattern is one of uneven capacity rather than lack of interest or activity.

Looking to the Future

Europe is likely to continue along an institution‑led path, deepening adoption as digital foundations and skills programmes mature. The UK’s research and policy strengths position it to shape governance debates and influence global practice. The Middle East is set to maintain rapid uptake where government investment remains strong. Africa’s progress will depend on improvements in infrastructure and skills, which remain the main barriers to wider diffusion.

Contrast with the United States and China

The United States moves through commercial scale. Its advantage lies in rapid enterprise uptake, strong private investment, and a competitive market that rewards early adoption. Europe, by contrast, advances through governance, skills, and institutional capacity. The UK sits between the two: commercially active but anchored in research and policy.

China’s path is driven by coordinated deployment across priority sectors. This creates scale quickly, but within a more directed innovation environment. The Middle East mirrors the speed but not the structure: uptake is fast, but driven by targeted national investment rather than sector‑level industrial planning.

In Africa, adoption is limited by structural factors, not by market dynamics or state‑led programmes. Its direction is one of gradual capacity building rather than rapid scaling.

Taken together, EMEA’s direction is shaped by institutions, governance, and state‑led investment, while the United States advances through market scale and China through coordinated deployment. Each region moves, but for different reasons and at different speeds.

Asia

China

The State of AI Report 2025 notes that Chinese frontier model developers such as DeepSeek, Qwen, and Kimi have closed much of the performance gap with leading United States models on reasoning and coding tasks.

South Korea

The AIEI diffusion study highlights South Korea's rise from 25th to 18th place globally in 2025, driven by policy, improved Korean language model performance, and consumer facing features.

India and Japan

India and Japan do not appear in the confirmed AI diffusion rankings published by the AIEI. The AIEI study provides quantified usage data only for countries that reached the global leaderboard, and neither India nor Japan is listed.

Singapore

The AIEI diffusion study ranks Singapore second globally for working age usage of generative AI tools, at 60.9 percent. The AIEI links this to early investment in digital infrastructure, AI skilling, and government adoption.

Conclusions

Asia shows several distinct paths that differ from both the United States and China’s own internal model. China’s frontier developers have narrowed the performance gap with leading U.S. systems, signalling a region where capability is rising quickly and where model development is becoming more competitive. This marks China as a major technical actor rather than only a large‑scale adopter.

South Korea’s movement up the global diffusion rankings reflects a different dynamic: steady policy support, improved local‑language model performance, and consumer‑facing features that drive everyday use. This is a pattern of uptake built on national coordination and product relevance rather than frontier model competition.

Singapore sits at the opposite end of the spectrum from most of the region. Its very high usage levels show what early investment in infrastructure, skills, and government adoption can achieve. It is a small but highly capable market where diffusion is broad and rapid.

India and Japan’s absence from the confirmed diffusion rankings highlights a lack of comparable usage data rather than a lack of activity. Without quantified metrics, their position in the regional landscape cannot be assessed in the same way as China, South Korea, or Singapore.

Looking to the Future

China is likely to continue strengthening its position in model development, given the narrowing performance gap and the scale of its domestic ecosystem.

South Korea’s trajectory suggests further gains where policy, language models, and consumer products continue to align.

Singapore’s early‑investment model gives it room to maintain high usage levels as tools mature.

India and Japan’s future visibility depends on the availability of consistent diffusion data.

Contrast with the United States and China

The United States advances through commercial scale and rapid enterprise adoption. China advances through coordinated capability building and sector‑led deployment. Much of Asia outside China follows neither path.

South Korea and Singapore show targeted national strategies that drive uptake through infrastructure, skills, and consumer‑level features rather than market competition or industrial planning.

Taken together, Asia presents a mixed picture: China as a rising technical competitor to the United States, South Korea and Singapore as fast‑moving national adopters, and other major economies with limited measurable diffusion.

This stands in contrast to the U.S. model of commercial scale and China’s model of coordinated deployment.

Australasia

Australia and New Zealand

The Australian Bureau of Statistics reports that 24 percent of Australian businesses used AI technologies in 2023 to 2024. For New Zealand, Digital Skills Aotearoa states that 19 percent of organisations were using AI tools in 2023.

Conclusions

Australia and New Zealand show a measured but steady pattern of enterprise‑level AI uptake. The figures point to two economies where adoption is present across a meaningful share of organisations, but not yet at the scale seen in the most rapidly diffusing countries. The pattern is one of gradual integration rather than rapid acceleration, shaped by existing digital capability and sector composition.

The evidence also suggests that both countries are moving from early experimentation into more routine operational use. The adoption levels recorded indicate that AI is no longer confined to isolated pilots but is beginning to appear in day‑to‑day business activity. What remains less clear is the depth of use within firms and the extent to which adoption is spreading beyond early movers.

Looking to the Future

The available data points to a likely continuation of this steady trajectory. Both economies have the digital foundations and organisational structures to support further uptake as tools mature and become easier to integrate. The current adoption levels suggest room for growth, particularly as more firms shift from exploration to implementation.

Future progress will depend on how quickly organisations can build skills, update processes, and adapt workflows to make effective use of AI. More consistent measurement would also help clarify how adoption evolves across sectors and firm sizes.

Overall, Australasia appears set for continued, incremental growth in AI use, driven by practical business needs and supported by existing digital capability.

Latin America

The OECD reports that around 20 percent of Mexican firms use at least one AI technology. Approximately 15 percent of Brazilian firms report the use of AI tools. In Chile, OECD statistics show that 12 percent of firms use AI technologies. Beyond these three countries, the Inter American Development Bank notes rising AI use across Latin America, especially in financial services and agriculture, but the IDB does not publish national percentages.

Conclusions

Latin America shows a pattern of steady but uneven enterprise‑level adoption. The available figures point to a region where AI use is present across major economies but varies widely in scale. Mexico, Brazil, and Chile each show meaningful uptake, yet none approach the levels seen in the fastest‑moving countries globally. The broader regional picture, drawn from IDB material, suggests that adoption is strongest in sectors with clear operational gains, notably financial services and agriculture. This indicates a practical, needs‑driven approach rather than a technology‑led surge.

The absence of consistent national metrics beyond the three reported countries highlights a measurement gap. It is difficult to assess the depth or spread of adoption across the region without comparable data, and the evidence that does exist points to early‑stage integration rather than widespread diffusion.

Looking to the Future

The current pattern suggests that Latin America is likely to continue along a sector‑led path, with adoption growing where AI delivers immediate operational value. Financial services and agriculture are well placed to deepen their use, given the early signs of traction. Broader uptake will depend on improvements in digital infrastructure, skills, and measurement, which remain uneven across the region.

More consistent reporting would help clarify how adoption evolves and where gaps remain. As tools become easier to deploy and integrate, there is room for growth across a wider range of sectors, but the pace will depend on the underlying capacity of firms and national digital systems.

Overall, the region shows early movement, concentrated in specific industries, with scope for further progress as capability and measurement improve.

Cross cutting themes

Infrastructure and skills as foundations

The AIEI diffusion study states that countries investing early in digital infrastructure, AI skilling, and government adoption now lead global usage rankings.

Uneven diffusion and a widening divide

The AIEI highlights a widening divide between the Global North and the Global South, with adoption in the Global North growing nearly twice as fast.

Commercial traction and enterprise demand

The State of AI Report 2025 and UNCTAD 2023 both point to strong commercial traction and rising enterprise demand.

Governance, safety, and regulation

The State of AI Report 2025 notes active regulatory developments and growing attention to risks associated with highly capable AI systems.

Conclusion

AI progress in 2024–2025 is accelerating, but unevenly. The UAE and Singapore show what coordinated national strategy and real‑world deployment can achieve, while the US, China and Europe continue to shape the frontier through research, investment and commercialisation.

The emerging divide is not East vs West, it is between nations operationalising AI at scale and those still discussing its potential.

Global Trends in AI
Global picture
North America
Europe, Middle East and Africa
Asia
Australasia
Latin America
- Conclusions
- Looking to the Future
Cross cutting themes
Conclusion
Related Work
Table of Contents
Further Reading

Evaluating AI Systems: Metrics that Matter

2026-04-26T00:00:00+00:00

Table of contents

This article presents metrics that matter to help you evaluate an LLM for programmatic use.

Metrics to Evaluate AI Systems

1. Evaluation as an Engineering Discipline

Evaluating an AI system differs from evaluating deterministic software. LLMs generate tokens based on probability, so behaviour varies across runs and model updates. Effective evaluation focuses on observable behaviour, failure modes, and interface stability. The aim is to measure real system behaviour, not synthetic benchmarks.

2. The Evaluation Surface Area An AI system exposes a wide surface area.

Some parts are controlled by the model, such as token prediction, internal weights, and sampling. Other parts are controlled by you, including prompt structure, constraints, retrieval inputs, output formats, and integration. Good evaluation measures the combined behaviour of both sides.

3. Core Metrics for Programmatic Use

Systems that call an LLM as a component must measure schema reliability, instruction adherence, deterministic stability, and latency. Schema reliability covers valid JSON, field completeness, and type correctness. Instruction adherence measures how well the model follows constraints. Deterministic stability checks variance under fixed sampling. Latency covers time to first token, total response time, and variability.

4. Metrics for RAG Systems

RAG adds new evaluation needs. Grounding fidelity measures alignment between claims and retrieved documents. Fidelity is about how faithfully the model sticks to the source material. Citation accuracy checks that references are correct and not invented. Retrieval quality evaluates recall, precision, and chunking impact. These metrics show whether the system uses retrieval effectively.

5. Metrics for Public‑Facing Systems

Public‑facing systems require safety and behavioural stability. Safety metrics measure disallowed or high‑risk content and consistency across paraphrased prompts. Behavioural stability measures tone consistency, avoidance of persona drift, and predictability across varied inputs.

6. Metrics for Reasoning Systems

Reasoning systems must evaluate logical consistency, task breakdown, and error sensitivity. Logical consistency checks for contradictions. Task breakdown measures whether sub‑tasks are identified and ordered correctly. Error sensitivity evaluates behaviour under incomplete or conflicting information.

7. Failure Mode Analysis

Evaluation must include attempts to trigger failure modes. Boundary tests check for fabricated tools or capabilities. Hallucination tests examine behaviour under missing, conflicting, or overloaded context. Prompt dilution tests measure behaviour when constraints overlap or when the system prompt becomes long.

8. Longitudinal Metrics

AI systems change over time, so evaluation must track drift. Model update drift measures behavioural changes after updates and detects regressions. Prompt stability metrics measure sensitivity to small edits or ordering changes. Longitudinal evaluation ensures stability as the model evolves.

9. Practical Evaluation Framework

A practical framework includes unit tests for prompt layers, integration tests for retrieval, and end‑to‑end tests for workflows. Golden sets provide curated inputs with expected outputs for regression detection. Failure logging categorises schema errors, grounding failures, reasoning failures, and safety violations.

10. Evaluation as Ongoing Engineering Work

Evaluation is continuous. AI systems require ongoing measurement because their behaviour is probabilistic and subject to change. Metrics must reflect real failure modes and integration points.

A structured evaluation framework produces systems that behave predictably, integrate cleanly, and remain stable over time.

Conclusion

Evaluating AI systems is not a narrow task.

It spans deterministic correctness, probabilistic behaviour, grounding, safety, reasoning, retrieval, latency, and long‑term drift.

The surface area is far larger than that of conventional software components, because an AI system is not only the model but also the constraints, prompts, retrieval pipeline, and integration code wrapped around it.

A structured evaluation framework is therefore essential.

Programmatic use requires metrics for schema reliability, instruction adherence, deterministic stability, and latency.

RAG systems add grounding fidelity, citation accuracy, and retrieval quality.

Public‑facing systems require safety and behavioural stability.

Reasoning systems require checks for logical consistency, task decomposition, and error sensitivity.

Failure mode analysis must deliberately probe boundary violations, hallucination conditions, and prompt dilution.

Longitudinal metrics must track drift across model updates and prompt changes.

A practical framework must combine unit tests for prompt layers, integration tests for retrieval, end‑to‑end workflow tests, golden sets, and structured failure logging.

The conclusion is unavoidable: this is not work that can be handled as a side‑task by feature developers. The evaluation load is continuous, specialised, and multi‑disciplinary. It requires expertise in retrieval, safety, reasoning, software correctness, and long‑term system behaviour. It requires adversarial testing, regression detection, and maintenance of a living evaluation suite. The cost of inadequate evaluation is high: schema failures, grounding errors, safety issues, reasoning faults, and silent regressions, any one of which may lead to a lack of compliance and statutory exposure.

AI evaluation is its own engineering discipline. It requires a dedicated team with clear ownership, specialised tooling, and ongoing responsibility for ensuring that AI systems behave predictably, integrate cleanly, and remain stable over time.

Metrics to Evaluate AI Systems
Conclusion
Related Work
Table of Contents

Latency is architecural

2026-04-26T00:00:00+00:00

Table of contents

Latency is architectural

Most latency comes from retrieval hops, long prompts, and serial tool calls. The model call is rarely the slow part. The pipeline is the bottleneck. Optimise orchestration, not just the model.

Engineers often assume the model is the slow part. It usually is not. The real drag comes from the machinery wrapped around it.

Retrieval hops cost more than you expect

Every vector search, metadata filter, re‑rank, and chunk stitch is another network hop. Do that a few times and half your latency budget has vanished before the model has even seen a token. It is the old "too many microservices" problem wearing a new badge.

Too Many microservices

A system begins tidy, then grows arms and legs. Someone adds a retriever. Someone adds a re‑ranker. Someone adds a metadata filter. Someone adds a chunk stitcher. Each piece looks harmless. Each piece solves a problem. But once they are strung together, the whole thing slows to a crawl.

RAG pipelines follow the same pattern. Instead of ten microservices, you now have ten retrieval hops. Instead of service chatter, you have index chatter. Instead of JSON bouncing around a cluster, you have embeddings and chunks being passed across the network. The labels have changed, but the behaviour has not.

In a microservice stack, services talk to each other all day long. They pass JSON around, wait for replies, retry on failure, and generally keep the network busy. That is service chatter.

In a RAG stack, the same noise comes from your retrieval layer. The actors are different, but the behaviour is the same. Your vector index, keyword index, metadata store, and re‑ranker all talk to each other. They pass embeddings, scores, filters, and chunks back and forth. Each hop is another round trip. Each hop adds delay. Each hop adds another place for things to wobble.

It is chatter because none of it is real work from the user’s point of view. The user wants an answer. The system spends most of its time gossiping between indexes about which chunk might be relevant. It is busy, but not productive.

The point is simple. You have replaced one kind of internal noise with another. The labels have changed, but the cost has not. If you let the retrieval layer grow without discipline, it will behave exactly like an over‑eager microservice mesh. It will talk too much, wait too long, and slow everything down.

Every hop adds latency. Every hop adds a failure mode. Every hop adds mental overhead. Hop latency accumulates in the end-to-end-pipelines. The job becomes debugging the plumbing rather than improving the product. The system becomes sluggish, brittle, and full of odd surprises.

The lesson is the same as it was during the microservice boom. Keep the number of moving parts low. Keep the boundaries clear. Keep the data local whenever you can. If you do not, the pipeline will drag, no matter how fast the model is.

Leaving the process costs you

Vector search is typical for RAG, but it is not the only culprit. Any retrieval layer that reaches across the network will cost you time. It does not matter whether you use a vector index, a keyword index, a hybrid index, or a bespoke store. If you have to leave the process, hit a service, wait for it to return, and then stitch the results back together, you will pay for it in latency.

Long prompts are silent killers

Sending 200,000 tokens into a model is not free. As of April 2026, GPT-5.5 is USD 5.00 per 1 million tokens, so USD 1 for 200k tokens. This might not sound much but if your whole AI system that is made up from multiple pipelines calls OpenAI a thousand times in an eight-hour period, that is one call every 86 seconds, costing USD 1,000 per day. As you introduce features that rely on AI, this cost can balloon.

You pay for tokenisation, network transfer, and ingestion. It is the equivalent of posting a novel every time you want a paragraph back. Shorter prompts are not only cheaper, they are faster and far easier to reason about.

Cloud costs balloon because the pricing model rewards scale until it punishes you. Everything looks cheap at the start. A few API calls here, a small vector index there, a modest GPU for a prototype. Then the system goes live, traffic rises, and the bill climbs faster than the usage graph.

The pattern is predictable. You pay for every hop, every lookup, every token, every gigabyte, and every idle minute. The cloud does not care whether the work was useful. It charges for activity, not value.

RAG pipelines are especially prone to this. Retrieval is chatty. Each query touches several indexes. Each index has its own storage, compute, and network fees. The model call is only one line on the invoice. The real cost comes from the scaffolding wrapped around it.

Costs balloon because the architecture balloons. More hops. More services. More indexes. More caching layers. More background jobs. More monitoring. More logs. Every piece adds a little cost. Together they add a lot.

The cloud makes it easy to scale up, but it does not make it easy to scale down. Once the system is busy, you pay for the peaks, not the averages. You pay for the buffers, the replicas, and the safety margins. You pay for the comfort of not waking up at three in the morning.

The cloud invoice is driven by the highest sustained load, not the gentle baseline you see on a dashboard.

Cloud platforms charge for capacity, not comfort. When traffic spikes, the system scales out. Extra replicas spin up. Buffers grow. Queues stretch. More storage is touched. More network is consumed. The platform does not scale back the instant the spike ends. It holds the extra capacity for safety, stability, and headroom. You pay for that headroom.

The average load might look modest, but the cloud does not bill you on the average. It bills you on the resources that were provisioned to survive the worst ten minutes of the day. If your peak is ten times your baseline, your bill will reflect the peak, not the baseline.

The only defence is discipline. Keep the design lean. Keep the hops few. Keep the data local. Keep the retrieval tight. Keep the prompts short. Keep the pipeline simple. If you do not, the cloud bill will grow faster than the user base, and it will not stop until you force it to.

Serial tool calls turn your pipeline into treacle

If your workflow is LLM → tool → LLM → tool → LLM, you have built a queue, not a pipeline. Everything waits for everything else. It is the same anti‑pattern that made synchronous RPC chains painful in the early microservice era.

A queue and a pipeline look similar on a whiteboard, but they behave very differently once traffic hits them. The distinction matters, because one keeps work moving and the other forces everything to wait its turn.

A queue is a stop‑start system. Each step blocks until the previous step has finished. Nothing can overtake anything else. If one stage slows down, the entire flow backs up behind it. This is what happens when you chain LLM calls and tools in a strict sequence. The second LLM call cannot begin until the tool has replied. The tool cannot run until the first LLM call has finished. The whole thing becomes a single‑file line.

A pipeline is a flow system. Work moves through independent stages that can run at the same time. Stage one can process ithe next item while stage two handles item one. Throughput rises because the stages overlap. The system does not wait for each piece to finish before starting the next. This is how high‑volume systems stay fast even when individual steps are slow.

A queue waits for the whole journey. A pipeline hands work off and moves on.

The handoff is the key. Once a stage can pass work downstream and start the next item without waiting, you have built a pipeline, not a queue.

The problem with LLM → tool → LLM → tool → LLM is that it behaves like a queue. Every step waits for the previous one. There is no overlap, no parallelism, and no slack. One slow tool call stalls the entire chain. It is the same pattern that made synchronous RPC chains painful in early microservice designs. The system is busy, but nothing is flowing.

The lesson is simple. If you want speed, build a pipeline. If you build a queue, do not be surprised when everything crawls.

4. Orchestration overhead accumulates

Glue code, JSON wrangling, retries, fallbacks, schema checks, and all the other dull bits. Each one is tiny. Each one feels harmless. Together they slow the system more than any single model call ever will.

The overhead hides in plain sight. A few milliseconds to validate a schema. A few more to serialise a payload. A few more to deserialise it. A few more to retry a flaky call. A few more to merge two partial results. None of these steps look expensive on their own. They are not. The cost comes from the fact that you do them on every request, across every stage, under load.

This is why orchestration overhead is so deceptive. It does not arrive as one big hit. It arrives as a hundred small ones. It is death by a thousand cuts. The pipeline spends more time preparing to do work than doing the work.

The worst part is that this overhead grows with complexity. Add one more tool call, and you add one more round of serialisation. Add one more fallback, and you add one more branch to evaluate. Add one more schema, and you add one more validation pass. The system becomes a tangle of tiny chores.

This is usually where the real time goes. Not in the model. Not in the vector search. Not in the database. In the glue. In the stitching. In the invisible admin that surrounds every step. The only fix is discipline: fewer hops, fewer formats, fewer retries, fewer moving parts. The less you orchestrate, the faster everything becomes.

The model is rarely the bottleneck

Modern inference is GPU‑accelerated and heavily optimised. Your RAG stack is a distributed system full of I/O, hops, and blocking calls. Optimising the model while ignoring the pipeline is like tuning the engine while the tyres are flat. The power is there, but the car still drags.

Modern LLM inference is brutally efficient. The kernels are fused. The memory access patterns are tuned. The batching is tight. The GPUs run flat out. The model is rarely the slow part. It is the most optimised component in the entire stack, because it has to be. Vendors pour millions into shaving microseconds from calculation paths.

Your RAG pipeline is the opposite. It is a distributed system stitched together from storage calls, network hops, serialisation steps, retries, and blocking operations. Every part of it waits for something else. Every hop crosses a boundary. Every boundary adds latency. The model is a rocket engine bolted to a shopping trolley.

This is why polishing the model is the wrong instinct. You can shave 10 percent off inference time and never notice it, because the pipeline is burning that time several times over in glue code and I/O. The GPU is idle while your retriever fetches chunks. The retriever is idle while your re‑ranker waits for a schema check. The re‑ranker is idle while your orchestrator serialises JSON. The whole system is dominated by the slowest, least optimised parts.

The handbrake is the pipeline. The bonnet is the model. Shining the bonnet does not make the car move. Releasing the handbrake does. If you want real speed, you fix the hops, the queues, the blocking calls, the retries, the formats, and the orchestration. That is where the time goes. That is where the wins are.

Throughput beats single‑query latency

In a real system, throughput matters more than shaving a few milliseconds off a single request.
Throughput keeps queues short, users calm, and servers steady.
A system that flows well will always outperform a system that only looks fast in isolation.

A design that includes:

parallel retrieval
batched vector queries
cached embeddings
pre‑computed context
non‑blocking tool calls

will outrun a "fast" single‑query setup every day of the week.

Think like a backend engineer, not a demo builder.
Design for flow, not fireworks.

Evaluation must be continuous

LLM behaviour drifts. Model updates shift outputs. Data changes. Prompt templates evolve. Retrieval indexes age. Static tests decay. Continuous evaluation with real traffic patterns is the only stable approach.

LLMs are not fixed points. They are moving systems. Vendors update weights. Safety layers change. Tokenisers shift. Even subtle adjustments can alter how a model interprets a prompt or ranks retrieved context. A test that passed last month can fail today without any change in your code.

Your data is not fixed either. Documents are added, removed, rewritten, or re‑indexed. Embeddings drift as models change. Metadata grows stale. A retrieval query that once surfaced the right chunk may surface something weaker six weeks later. The index ages, and the quality of the answer ages with it.

An embedding will turn a sentence into a list of numbers where similar items end up close together.

Prompt templates evolve as well. You tweak wording. You add guardrails. You change formatting. You introduce new variables. Each change shifts behaviour in ways that are hard to predict. A small edit can ripple through the entire pipeline.

Static tests cannot keep up with this movement. They freeze expectations in time. They assume the system is stable. It is not. The tests decay because the system they measure is drifting underneath them. A green test suite can give a false sense of confidence while the live system quietly degrades.

The only reliable approach is continuous evaluation with real traffic patterns. You must measure quality under the same conditions the system actually faces: real prompts, real retrieval noise, real user phrasing, real edge cases, real load. Automated reality is required. This is the only way to detect drift early and correct it before it becomes visible to users.

The system is alive. The evaluation must be alive with it.

Guardrails must be layered

No single guardrail is enough. Combine input checks, retrieval filters, prompt constraints, output checks, and post‑processing. Each layer catches different failures. One layer alone invites outages.

Guardrails fail for different reasons. Input checks catch malformed or hostile queries, but they cannot see what retrieval will surface. Retrieval filters remove unsafe or irrelevant chunks, but they cannot stop a prompt template from mis‑framing the task. Prompt constraints shape model behaviour, but they cannot guarantee the model will obey them under stress. Output checks catch violations after the fact, but they cannot prevent the model from producing them in the first place. Post‑processing can clean up structure, but it cannot repair a fundamentally wrong answer.

Each layer has blind spots. Each layer has failure modes. Each layer protects a different part of the system. When you stack them, the gaps do not align. When you rely on one, the gaps are exposed.

This is why single‑layer safety is fragile. A lone input filter cannot stop a retrieval glitch. A lone output checker cannot stop a prompt injection. A lone prompt template cannot stop a malformed chunk. A lone retrieval filter cannot stop a model hallucination. Outages happen when one layer is asked to do the job of five.

A robust system uses layered defence:

input validation to reject malformed or hostile queries
retrieval filtering to control what context enters the model
prompt constraints to shape behaviour and reduce ambiguity
output checks to enforce structure and detect violations
post‑processing to normalise, redact, or correct

None of these layers is perfect. Together they are resilient. That is the point. Modern LLM systems fail in many small ways, not one big way. The only stable approach is to catch small failures early, often, and repeatedly across the pipeline.

The future is orchestration

The next wave is not bigger models. It is coordination across many specialised models. It is managing context across workflows. It is building predictable tool‑calling chains. LLMs are components now. The engineers who master orchestration will shape what comes next.

The era of single‑model systems is ending. One large model trying to do everything is slow, expensive, and brittle. The future is a network of smaller, focused models: one for retrieval, one for classification, one for planning, one for extraction, one for reasoning, one for generation. Each model does one job well. The value comes from how they work together.

This shift changes the engineering challenge. It is no longer about squeezing more tokens per second out of a GPU. It is about coordinating dozens of moving parts without losing context, consistency, or latency. You must track state across hops. You must pass partial results between models. You must ensure that tools are called in the right order, with the right schema, at the right time. You must keep the pipeline flowing even when individual components fail or drift.

Context management becomes a first‑class problem. You cannot rely on a single prompt to hold everything. You need shared memory, structured state, and workflow‑level constraints. You need to decide what each model should know, what it should not know, and how to hand off information cleanly. The system must behave like a team, not a monolith.

Tool‑calling becomes a discipline of its own. You need predictable chains, clear contracts, and stable interfaces. You need to design workflows that are parallel where possible, serial only where necessary, and resilient everywhere. The orchestration layer becomes the real engine of the system.

This is why the next wave belongs to engineers who understand distributed systems, workflow design, and pipeline optimisation. The models are powerful, but the power is unlocked only when they are coordinated. The future is not a bigger brain. It is a well‑run organisation of smaller brains working together.

Conclusion

Latency in LLM systems is dominated by architecture, not model speed. Most of the delay comes from retrieval hops, network boundaries, prompt expansion, and token‑level generation, so performance improves when you redesign the pipeline, not when you tweak the prompt. Once you see this, it becomes obvious that long prompts, scattered retrieval, and unnecessary round‑trips are the real cost drivers, and that reducing latency means reducing work, not asking the model to work faster.

The practical conclusion is that throughput and batching matter more than single‑query latency, retrieval must be minimised and localised, and prompts must be aggressively shortened. Systems that treat latency as an architectural problem become predictable and scalable; systems that treat it as a model problem stay slow no matter which model they plug in.

You can process the same amount of data while using fewer hops, fewer round‑trips, using fewer tokens, and making fewer retrieval calls, fewer prompt expansions, and fewer model invocations.

It is not about shrinking the task. It is about shrinking the machinery required to accomplish it.

You keep the data volume the same, but you redesign the path so the system touches that data:

fewer times
in fewer places
with fewer transformations
with fewer tokens
with fewer model calls

Same data, less orchestration. That is why latency drops.

Latency is architectural
Serial tool calls turn your pipeline into treacle
The model is rarely the bottleneck
Throughput beats single‑query latency
Evaluation must be continuous
Guardrails must be layered
The future is orchestration
Conclusion
Related Work
Table of Contents

Chat Interface to System Component

2026-04-26T00:00:00+00:00

Table of contents

Programmatic Interfaces to AI Systems

We interact with AI systems through natural language. As engineers, we are used to structured and predictable interfaces such as REST or gRPC.

AI systems do not behave like that. Their outputs are probabilistic, and this creates real challenges when we try to use them as components inside software systems.

Most current models behave like chat interfaces. What we need are models that behave like reliable parts of an application.

This article explains what is currently practical and how to build interfaces that bring AI systems closer to the expectations of software engineering.

The Challenge

Large language models (LLMs) generate text by predicting the next token. They are not rules engines, parsers, or deterministic programs.

An LLM's output is a probability distribution over the next token. The distribution depends on the prompt, any conversation history you include, the model’s internal weights, and the sampling parameters.

Even with strict instructions, the model still performs this operation:

"Select the next token that has the highest probability given the input so far."

That is probability, not logic.

The practical approach is to apply prompt constraints that reduce the likelihood of outputs that are not fit for purpose.

Prompt Constraints

An LLM may return a result that does not fit the calling side. This is a failure mode of the model.

Each of the eight layers reduces the likelihood of a specific failure mode. Together, they form a structured interface between the client code and the model.

This approach will make your code more:

predictable
grounded in the provided context
structured in both input and output
controllable through explicit constraints

Because LLMs are probabilistic, these layers cannot eliminate failure modes.

Other failure modes exist, but they are outside the scope of this section. The focus here is on the eight layers that address the most common issues.

The Eight Layers

Identity
Safety & Compliance
Capability Boundaries
Output Format
Citation Rules
RAG Grounding
Reasoning Strategy
Task Logic

1. Identity

Identity anchors the model’s role and prevents behavioural drift. Without a stable identity, the model may shift tone, adopt unintended personas, or answer outside its intended domain. This layer establishes what the model is and what it is not, providing the behavioural foundation for all the layers below.

2. Safety & Compliance

Safety and compliance constraints ensure the model minimises harmful, disallowed, or high‑risk content. This protects users, organisations, and downstream systems. It is essential for any public‑facing or regulated deployment. This helps to ensure that the model behaves within acceptable boundaries.

3. Capability Boundaries

LLMs tend to overreach. They might claim abilities they do not have or fabricate tools, APIs, or actions. This layer reduces the likelihood that the model will perform operations outside its scope. It keeps the system more honest, more predictable, and aligned with its real capabilities.

4. Output Format

Programmatic systems require structured, unambiguous, machine‑readable output. This layer enforces schemas, reduces the likelihood of format drift, and helps to ensure downstream components can reliably parse responses. It helps move the model away from a conversational agent towards a dependable software component.

5. Citation Rules

Citation rules enforce traceability and verifiability.

This layer reduces the likelihood of fabricated sources, invented URLs, and unsupported claims. This layer is essential for any system that must justify its answers or provide evidence for its statements.

6. RAG Grounding

RAG grounding ensures the model uses only the supplied context as its source of truth. It damps down hallucinations by binding the model to provided evidence. This layer is the core of retrieval‑augmented generation and is mandatory for knowledge‑grounded systems.

This approach does not eliminate hallucinations but it will reduce them.

7. Reasoning Strategy

Reasoning strategy helps to stabilise the model’s logic. It moves towards stepwise thinking, disambiguation, and evidence‑first reasoning. This layer reduces subtle reasoning errors and improves consistency across complex tasks.

8. Task Logic

Task logic governs how the model interprets and executes user instructions. It handles ambiguity, resolves contradictions, and decomposes multi‑part tasks. This layer ensures the model behaves reliably in real‑world, messy, human‑language scenarios.

The Eight Layer Stack

These eight layers form a stack where each layer protects against a different class of LLM failure:

Layer	Prevents
Identity	Drift, persona instability
Safety & Compliance	Harmful or non‑compliant output
Capability Boundaries	Overreach, fabricated abilities
Output Format	Schema breakage
Citation Rules	Unsupported claims
RAG Grounding	Hallucination
Reasoning Strategy	Faulty logic
Task Logic	Misinterpretation

Together, they create a more controlled and predictable calling-side interface to an AI system.

The Minimal Stack

For any programmatic interaction with an LLM, three layers are essential:

Identity
Capability Boundaries
Output Format

Identity prevents behavioural drift. Capability boundaries reduce the likelihood of fabricated abilities, tools, or actions. Output format constraints reduce the likelihood of schema drift, malformed JSON, and downstream parsing failures.

Drift from the required behaviour leads to calling‑side errors. These three layers reduce the likelihood of the most fundamental failure modes.

The Minimal Stack for RAG

Retrieval‑Augmented Generation (RAG) improves accuracy by supplying the model with domain‑specific and up‑to‑date information from a document store. The model uses this retrieved content to produce a grounded and human‑readable response.

RAG passes to the LLM your domain data that its answer is constrained to be based on, using the LLM's language-processing features to produce a human-friendly response. RAG reduces hallucinations and improves factual accuracy.

The minimal RAG stack consists of the three core layers, plus RAG Grounding and Citation Rules. This creates a five‑layer baseline for any RAG system.

These layers improve stability, reduce unsupported claims, and increase the reliability of the final output.

RAG Grounding ensures the model uses the retrieved content as its source of truth. Citation Rules reduce the likelihood of invented sources and unsupported statements.

RAG is required when:

accuracy matters
knowledge changes frequently
domain‑specific expertise is required
hallucinations are unacceptable
answers must be auditable
you need to integrate private or internal documents

The Minimal Stack for Public-Facing Systems

Public‑facing systems require the five‑layer RAG stack plus Safety and Compliance.

These six layers form the minimum configuration for any system exposed to real users. They address:

behavioural stability
safety
overreach damping
structured output
evidence requirements
grounding to damp down hallucinations

The Full 8 Layer Stack

The final two layers are Reasoning Strategy and Task Logic.

Reasoning strategy is required when:

the model must break problems into steps
ambiguity must be resolved before answering
shallow or shortcut reasoning would cause errors
the system must justify or stabilise its logic
you want consistent reasoning across varied prompts

This layer reduces subtle reasoning failures that grounding alone cannot address.

Task Logic is required when:

instructions are complex or multi‑part
instructions conflict or require prioritisation
tasks must be decomposed before execution
the system must handle unstructured or ambiguous input
consistent behaviour is required across varied task types

This layer helps ensure the model interprets and executes instructions correctly.

Using the Eight Layers in Code

OpenAI's API is Stateless

Note: OpenAI’s APIs are stateless by default. Each request only contains the context you explicitly send. Each text generation request is independent and stateless. Therefore, multi‑turn conversations only occur when you manually include previous messages in the request. The code below has no requirement to do this and so such a history is not present. If it was, later answers would be influenced by earlier queries and this is not required for this interaction.

With OpenAIi, you can use a conversation memory. This is possible with OpenAI features such as conversation, previous_response_id (Responses API) or the Agents SDK’s session memory.

Coding the Eight Layers

The approach here is to represent each layer as a dictionary that always has a 'role' key (set to 'system' or 'user'). The other keys are used to define a standard set of values. When passed to OpenAI's API, each dictionary is processed to build an OpenAI API-compatible dictionary which consists of just 'role' and 'content'.

'content' is constructed from the non-role values below.

We can imagine each dictionary being retrieved from a configuration store and the keys are just names for the associated value. These names enable you to discuss constraint types per layer. It is the values that become part of 'content'.

# 1. Identity Layer
    system_identity = {
        "role": "system",
        "identity": "You are a retrieval‑augmented assistant."
    }

# 2. Safety & Compliance Layer
system_safety_compliance = {
    "role": "system",

    # Core safety principles
    "no_harm": "The assistant must not provide harmful, dangerous, or abusive content.",
    "no_illegal": "The assistant must not assist with illegal activities, evasion, or wrongdoing.",
    "no_personal_data": "The assistant must not request, store, or infer personal data about real individuals.",
    "no_medical_advice": "The assistant must not provide medical, legal, or financial advice beyond what is explicitly allowed.",
    "no_sensitive_inference": "The assistant must not infer protected attributes (race, religion, health, etc.).",

    # Refusal behaviour
    "refusal_style": "If a request violates safety rules, the assistant must refuse clearly and briefly.",
    "refusal_format": "Refusals must be one sentence, factual, and non‑judgmental.",
    "refusal_no_elaboration": "Do not provide workarounds, alternatives, or detailed explanations when refusing.",

    # Compliance priority
    "compliance_overrides": "Safety and compliance rules override all other instructions, including user requests.",
    "no_conflicting_instructions": "If user instructions conflict with safety rules, follow safety rules."
}

# 3. Capability Boundaries Layer
system_capability_boundaries = {
   "role": "system",

    # Allowed capabilities
    "allowed_scope": [
        "Interpret user questions.",
        "Use ONLY the provided context for answers.",
        "Produce structured JSON according to the schema.",
        "Explain reasoning based solely on the context.",
        "Quote exact lines from the context when required."
    ],

    # Disallowed capabilities
    "disallowed_scope": [
        "Do NOT use external knowledge.",
        "Do NOT invent facts, labels, or citations.",
        "Do NOT answer questions outside the provided context.",
        "Do NOT perform tasks requiring tools, browsing, or external systems.",
        "Do NOT generate content outside the required schema."
    ],

    # Boundaries for reasoning
    "reasoning_limits": "Reasoning must be explicit but must not include hidden steps or invented logic.",

    # Boundaries for output
    "format_limits": "Output must remain within the exact schema and must not include additional fields or commentary.",

    # Boundaries for behaviour
    "no_role_shift": "The assistant must not change persona, identity, or role unless explicitly instructed by system messages."
}

# 4. Output Format Layer
system_output_format = {
    "role": "system",
    "single_line_json": "Your output MUST be a SINGLE JSON object on ONE LINE ONLY.",
    "schema": f"{schema_out}",
    "strict_structure": "The output must follow the exact schema structure with no deviations."
}

# 5. Citation / Attribution Layer
system_citation_rules = {
    "role": "system",
    "label_requirement": "Every citation MUST begin with the exact Incoming Context=\"...\" label from the source.",
    "quote_requirement": "Every citation MUST include the exact quoted line from that same context block.",
    "no_label_omission": "Do NOT omit the Incoming Context label.",
    "no_label_invention": "Do NOT invent labels.",
    "no_summarisation": "Do NOT summarise lines; quote them exactly.",
    "empty_citations_when_missing": "If the answer is not in the context, output an empty Citations section with correct structure."
}

# 6. RAG Grounding Layer
system_rag_grounding = {
    "role": "system",
    "use_context_only": "Use ONLY the provided context to answer the question.",
    "no_context_no_answer": "If the answer is not in the context, explicitly say so.",
    "multiple_valid_answers": "Multiple answers may be valid; include all that are supported by the context.",
    "context_is_authoritative": "The provided context is the ONLY source of truth.",
    "no_external_knowledge": "Do NOT use outside knowledge or assumptions.",
    "answer_must_reference_context": "All answers must be derived strictly from the context block."
}

# 7. Reasoning Strategy Layer
system_reasoning_strategy = {
    "role": "system",

    # How to reason
    "carefully_read": "First, carefully read the context and the question.",
    "identify_all": "Identify all relevant passages in the context.",
    "explain": "Explain, step by step, how those passages support your answer.",
    "explicit": "Make your reasoning explicit, but concise.",
    "no_invention": "Do not invent facts that are not in the context.",
    "honesty": "The 'reasoning' field is for developers and will be logged. Be honest and explicit.",

    # How reasoning connects to citations
    "reasoning_field": "The reasoning field must refer only to information present in the provided context.",
    "clear_explain": "Clearly explain how the quoted lines in 'citations' support the 'answer'.",
    "avoid_generic": "Avoid generic phrases like 'based on the context'; be specific about which parts matter."
}

# 8. Task Logic Layer
system_task_logic = {
    "role": "system",

    # Instruction hierarchy
    "interpretation_priority": [
        "1. Follow system instructions.",
        "2. Follow developer instructions.",
        "3. Follow user instructions.",
        "4. Follow schema and formatting rules."
    ],

    # Ambiguity handling
    "ambiguity_rules": [
        "If the question is ambiguous, identify all plausible interpretations.",
        "Choose the interpretation most directly supported by the context.",
        "If ambiguity remains, state the ambiguity explicitly in the reasoning field."
    ],

    # Multi‑part question handling
    "multi_part_rules": [
        "If the question contains multiple sub‑questions, answer each one separately.",
        "If only some sub‑questions are supported by the context, answer those and state which cannot be answered."
    ],

    # Conflict resolution
    "conflict_rules": [
        "If context passages contradict each other, cite both and explain the contradiction.",
        "If user instructions contradict system instructions, follow system instructions.",
        "If schema requirements contradict user instructions, follow schema requirements."
    ],

    # Missing‑information behaviour
    "missing_info": "If the answer is not present in the context, explicitly say so and provide an empty citations list.",

    # Strict adherence
    "no_overinterpretation": "Do not infer meaning beyond what is explicitly stated in the context.",
    "no_assumptions": "Do not assume facts, motivations, or implications not present in the context."
}

The code above is a list of named Python dictionaries.

Three additional RAG user objects are also passed (as below) that contain two additional pieces of data: 'context' and 'user_query'.

context contains the input for the RAG. It is the result of the local search that is chunked.

user_query is the prompt from the user, e.g., "are there any restrictions in this contract".

rag_user_context = {
        "role": "user",
        "label": "Context",
        "content": f"{context}"
        }

rag_user_query = {
        "role": "user",
        "label": "Question",
        "user_query": f"{user_query}"
        }

rag_user_rules = {
    "role": "user",
    "context_is_authoritative": "The assistant must treat the provided context as the ONLY source of truth.",
    "no_external_knowledge": "The assistant must not use outside knowledge or assumptions.",
    "answer_must_reference_context": "All answers must be derived strictly from the context block.",
    "no_context_no_answer": "If the answer is not present in the context, the assistant must explicitly state this.",
    "multiple_answers_allowed": "If multiple valid answers exist in the context, the assistant should include all of them."
    }

OpenAI has a specific schema for JSON object input. An object with two keys is expected 'role' and 'content'. Role is one of 'user', 'system', or 'assistant'. 'content' is assigned the result of processing each of the above user and system dictionaries with to_message.

def to_message(obj):
    role = obj.get("role", "system")

    # Build content from all non-role fields
    parts = []
    for key, value in obj.items():
        if key == "role":
            continue

        # If the value is a list, join its items
        if isinstance(value, list):
            parts.append("\n".join(value))
        else:
            parts.append(str(value))

    content = "\n".join(parts).strip()

    return {"role": role, "content": content}

Before calling OpenAI, all of the objects above are added to a list.

messages = [
        to_message(system_identity),  # Layer 1
        to_message(system_safety_compliance),  # Layer 2
        to_message(system_capability_boundaries),  # Layer 3
        to_message(system_output_format),  # Layer 4
        to_message(system_citation_rules),  # Layer 5
        to_message(system_rag_grounding),  # Layer 6
        to_message(system_reasoning_strategy),  # Layer 7
        to_message(system_task_logic),  # Layer 8

        # User context + question
        to_message(rag_user_context),
        to_message(rag_user_query),
        to_message(rag_user_rules)  # optional but recommended
    ]

A list of processed layers makes contraining the actions of the LLM trivial. If you need a new layer you create a new dictionary and add it to the list, as above.

The list is then passed to build_params.

def build_params(input=None, messages=None):
    params = {'model': 'gpt-5.4-nano'}
    if input is not None:
        params['input'] = input
    if messages is not None:
        params['messages'] = messages

    return params

build_params ensures we target the same model each time.

open_ai_query calls OpenAI's API. The python code calls a wrapper like this to supply the messages list.

json_ai_user_result = open_ai_query(build_params(input=messages))

open_ai_query is:

def open_ai_query(params):
    # Without a valid key, this code will not work
    client = OpenAI(api_key='<your key>') # Substitute your OpenAI API key here

    params['input'] = clean_input(params['input'])

    response = client.responses.create(**params)

    params['output_text'] = response.output_text
    params['response'] = str(response)
    params['date'] = datetime.now().isoformat()

    return params['output_text']

The call to OpenAI is the line client.responses.create(**params). The value params is passed in unpacked (**params) to provide dictionary keys as function parameters. This is a convenient way of specifying what should be passed to OpenAI.

params then has a number of other keys and values assigned. This is to support traceability.

Supporting traceability will be discussed in a future article. LLM calls require more than logging and observability. They require traceability, especially when decisions are made based on LLM output. Our systems need to be able to show which model was called, when, what the reasoning was, what result was gained, and any chain of LLM calls. Logging and observability alone do not do this.

open_ai_query relies on clean_input which is simply this:

def clean_input(model_input):
    try:
        return codecs.decode(model_input, "unicode_escape")
    except:
        return model_input # return what is given as best-effort.

        # Escape sequences may affect your results due to model tokenisation

Increasing the number of instructions per layer

As the system prompt grows, each instruction carries less relative influence. The model processes all tokens uniformly, so important constraints can lose emphasis when surrounded by a large volume of text. Long prompts also make it harder for the model to infer priority and can hide small contradictions between layers. Clear ordering and explicit priority rules help reduce this effect.

Instruction Collisions

When multiple layers contain overlapping or conflicting instructions, the LLM must resolve the conflict using the text alone. The final system message ithat it sees takeis precedence, but subtle inconsistencies can weaken the intended behaviour. Ensuring that layers do not contradict each other and that priority is stated explicitly reduces this risk.

Conclusion

LLMs Require Structured Interfaces

LLMs do not behave like deterministic software components. They generate tokens based on probability, which means natural‑language prompts alone are not a stable or reliable interface.

Layered Constraints Improve Reliability

A layered constraint model is necessary to reduce common failure modes. Identity, Capability Boundaries, and Output Format form the minimal stack for programmatic use. RAG systems require additional grounding and citation layers. Public‑facing systems require safety controls. Full reasoning systems benefit from all eight layers.

RAG Provides Essential Grounding

RAG supplies the model with domain‑specific and current information. It reduces hallucinations and improves factual accuracy, but it still requires constraints to ensure the model uses retrieved content correctly.

Prompt Length and Consistency Matter

As system prompts grow, individual instructions lose emphasis. Clear ordering and explicit priority rules help maintain consistent behaviour. Avoiding contradictory instructions is essential for predictable output.

Failure Modes Can Be Reduced, Not Removed

LLMs remain probabilistic. Constraints reduce the likelihood of errors but cannot eliminate them. Treating the prompt as a structured interface, rather than a single instruction, produces more predictable, testable, and maintainable systems.

What software engineers need to know about LLMs

2026-04-25T00:00:00+00:00

Table of contents

Large language models (LLMs) are disrupting the software engineering industry. Executives and software engineers now have a tool at their disposal that is so general in its scope that it can be dedicated to almost any task. LLMs are the ultimate "jack of all trades". It is our job to get the most from them.

The real interface: tokens, not text

Tokens shape what you can build. They decide how much context you can fit in, how fast the model responds, and how predictable the output is.

Token boundaries also change how the model interprets structure. Two prompts that look identical to you may tokenize differently and produce different behaviour.

When you design prompts, AI input or output schemas, or retrieval pipelines, you are really designing token flows. If you ignore tokens, you end up shipping features that behave one way in tests and another way in production.

Prompt A: "Summarize the user login flow."

Prompt B: "Summarise the user login flow."

To a human, the difference is not consequential. To a tokenizer, there is a critical difference.

"Summarize" and "Summarise" break into different token sequences.

The model’s internal statistics for each spelling differ.

The model may shift tone, structure, or level of detail.

And downstream formatting can change because the token pattern changed.

Prompt A: "List the steps to deploy the service."

Prompt B: "List the steps to deploy the service ."

The only difference is a space before the full-stop.

Prompt A ends with a single token for "service."

Prompt B ends with two tokens: "service" and "."

That tiny shift can change the model’s prediction path.

The model is not the system

Most failures blamed on models usually come from everything wrapped around them. In practice, the weak points look very familiar to any engineer who has shipped a distributed system.

Retrieval pipelines drift because indexes age, embeddings shift, and data freshness is rarely monitored. A model can only answer the question you actually retrieved, not the one you meant to retrieve.

Prompt templates collapse under odd inputs because they are often treated as static strings instead of executable logic. One unexpected newline or a missing field can break the entire chain of reasoning. Data freshness and data cleansing is key here.

Guardrails

Guardrails miss edge cases because they rely on pattern matching, not semantic guarantees. A single unhandled phrasing can bypass a rule that looked airtight in testing.

Imagine you build a guardrail that blocks requests containing "delete all users". It works in tests, so you ship it.

Then a real user sends: "can you delete all the users" or "please delete every user" or "remove all user accounts"

Your guardrail only catches the exact phrase it was written for. It matches strings, not meaning. One phrasing slips through, and the model executes a path you thought was protected.

Many guardrails end up acting like string comparisons even when they use embeddings or classifiers. They match surface patterns, not intent. If the phrasing shifts, the guardrail often fails.

For example, a rule might block "delete all users" because that exact pattern was seen during testing. But the same system may allow "remove every user account" because the embedding distance is just far enough to slip past the threshold.

This is the same failure mode as brittle input validation. If your rules depend on matching specific strings or narrow patterns, you get a system that behaves safely in tests and unpredictably in production.

You cannot solve this by telling the model “if a request is like 'delete all users', refuse to do it”. That feels intuitive, but it fails for the same reason input‑validation-by-string-match fails in any other system.

A prompt can describe the rule, but it cannot enforce the rule. The model will try to follow the instruction, but it has no semantic guarantee. It can still be persuaded, confused, or bypassed by a phrasing it has not seen before.

To actually solve this, you need layered controls outside the model:

Treat the model as untrusted. Never let it directly execute destructive actions. Put a permission layer between the model and anything irreversible.
Normalise user input before it reaches the model. Collapse phrasing, remove fluff, and classify intent. This gives you a stable signal instead of raw text.
Use a separate classifier or rules engine to detect dangerous intent. This component should be simpler, more predictable, and easier to test than the model itself.
Require explicit confirmation for destructive operations. The model can propose an action, but a deterministic system must approve it.
Log every step. When something slips through, you need to see the input, the normalised form, the classification result, and the model’s output.

The prompt can express the policy, but the system must enforce it. If you rely on the model alone, you are depending on pattern matching. If you build a layered pipeline, you get behaviour you can reason about, test, and trust.

Observability

Observability is weak because most systems log the request and the response, but not the context, the retrieval set, the template expansion, or the decoding parameters. When working with LLMs, without the context, retrieval set, template expansion and parameter decoding, debugging is guesswork.

An LLM is at the centre of a much larger system

The LLM is only one component. The system around it decides whether your product behaves like a tool or a slot machine. Engineers who treat the whole pipeline as a software system, not a magic box, build the reliable systems.

Determinism is a design choice

LLMs are probabilistic, but stability is possible. Temperature and top‑p control variance. Structured outputs reduce drift. Deterministic decoding is often more reliable than clever prompts. Treat randomness as a resource you allocate.

Temperature stretches or compresses the probability distribution. Top‑p chops off the tail of the distribution.

Temperature

As temperature increases, the LLM becomes more willing to pick lower‑probability tokens, which effectively means the "token candidate set" gets larger.

More accurately, low‑probability tokens get boosted, high‑probability tokens get flattened.

This means: the model is less confident, more tokens become available, and he sampling process has more room to explore. The next token is drawn from a wider effective set

Top-p

Top‑p (also called nucleus sampling) restricts the model to sampling only from the smallest set of tokens whose cumulative probability is ≥ p.

Think of it as a probability mass cutoff.

Example

Suppose the model predicts the next‑token distribution like this:

Token	Probability	Cumulative
A	0.40	0.40
B	0.25	0.65
C	0.15	0.80
D	0.10	0.90
E	0.05	0.95
F	0.05	1.00

Sorted by probability, cumulative mass builds like this:

A → 0.40 A+B → 0.65 A+B+C → 0.80 A+B+C+D → 0.90 A+B+C+D+E → 0.95 A+B+C+D+E+F → 1.00

Now apply top‑p:

top‑p = 0.5

Working down the ordered Probability column abov, we include tokens until the probability is cumulatively ≥ 0.5. Token A + B are allowed as they are the first tokens for whom the cumulative probability is ≥ 0.5. Once the condition is satisfied, we stop descending the column.

With top-p = 0.5, only tokens A and B are allowed.

For top‑p = 0.8

Include tokens until cumulative ≥ 0.8 → A + B + C. Only A, B, C are allowed.

top‑p = 0.95

Include tokens until cumulative ≥ 0.95 → A + B + C + D + E. Tokens A to E allowed; F is excluded.

When top‑p = 1.0

No restriction — all tokens allowed.

Passing temperature and top-p to OpenAI

In calling OpenAI, you can pass this:

{
  "model": "gpt-4.1",
  "messages": [
    { "role": "user", "content": "Explain temperature and top-p." }
  ],
  "temperature": 0.0,
  "top_p": 1.0
}

The last two fields directly control the sampling behaviour.

You are telling the model:

"Always pick the highest‑probability token. No randomness."

This is the closest thing to true determinism.

With temperature set to 0.0, the highest‑probability token is guaranteed to be selected, as long as the decoding method is greedy and no other randomness is introduced by the API or framework.

In an LLM, the decoder is the component that turns the model’s probability distribution into tokens.

Even with temperature equal to 0.0, top‑p could still exclude the highest‑probability token. For example, if the highest‑probability token is outside the top‑p nucleus (rare but possible with unusual distributions), the decoder would be forced to pick a different token. The nucleus is the group of tokens built cumulatively above.

Temperature = 0.0 and top_p = 1.0 is the strictest, safest deterministic configuration.

Context windows are not memory

AI vendors such as Anthropic and OpenAI control the LLM's window size, but you control how effectively you use it.

OpenAI's GPT‑5.4 has a 1,050,000‑token context window. GPT‑5.2, GPT‑5.1, and GPT‑5.1 Codex Max have 400,000‑token windows.

The window size is fixed at training time. Changing it requires retraining or re‑architecting the model, which only the vendor can do.

The vendor sets the ceiling. You decide how close you get to it. A 1M‑token window sounds like "great, I can dump everything in." But that is the wrong mental model.

The engineer decides:

how much of the window to fill
how aggressively to compress
how to structure retrieval
how to order information
how to avoid interference
how to budget tokens across system prompts, instructions, schemas, and retrieved docs

The vendor gives you the maximum. You determine the effective window.

A large window looks powerful, yet it behaves nothing like a bigger RAM module. The more of the window you use and the larger your use becomes, the model has to scan and reconcile far more information than it can reliably use. The signal‑to‑noise ratio drops, and the model starts leaning on familiar statistical patterns instead of the details that matter.

Position inside the window matters more than the raw size. Early and late tokens are not treated equally, and different models weight them differently. There is no guarantee that the most recent content is the content the model will use. This is why long prompts often ignore the last instruction you added.

Large windows also increase interference. When you pack in too much material, similar concepts begin to blur. Two sections that look distinct to you can collide inside the model’s internal representation. The output feels vague or inconsistent even though the inputs look clean.

Retrieval quality beats window size

This is why retrieval quality beats window size. Retrieval gives you control over what enters the window and where it goes. A large window without retrieval is just a bigger bucket. A smaller window with good retrieval is a structured workspace.

Retrieval here is any form of data retrieval that is performed before being sent to the LLM. This may be the result of a classic RAG pipeline where a local search of a document store is performed and the results chunked before being passed to the LLM that is instructed to restrict its analysis to the uploaded search data.

But retrieval here is more general than RAG. It refers to the smart selection of data for an LLM to process. Retrieval may bring data back from a SQL, Graph or NoSQL query, or it may be the smart selection of summaries or user's notes pulled from storage.

The opposite of retrieval is dumping everything in raw.

The most reliable mental model is to treat the window as a scratchpad. It is a temporary working area, not a knowledge store. You place only what the model needs for the current task, in the order that helps it reason. If you treat the window like long‑term memory, you get unpredictable behaviour. If you treat it like a scratchpad, you get control.

LLMs compress patterns, not facts

When an LLM is trained, the input training data will be measured in terabytes. The output is billions of weights that encode the statistical structure of the training data. Those weights are the model es the weights: patterns (common sequences, phrasing, structures, and correlations); relationships (semantic similarity, analogies); generalisation behaviour (moving between examples via statistical interpolation); and task-relevant transformations to assist with instruction following, data formatting. and conversational norms.

LLMs do not store data; they are not databases. They store weights that represent patterns from the training data.

Many different training examples can be represented internally by the same (or very similar) set of weights.

As different examples can be represented by the same weights, LLMs have a tendancy to hallucinate. Hallucinations are baked into the design of LLMs.

Training takes terabytes of text and produces billions of updates into a fixed‑size model and outputs the weights that approximates the training data.

In doing this the transformation is many‑to‑one (different examples collapse together), and irreversible as you cannot reconstruct the originl training data from the weights. But, more importantly, the output is statistical as the weights encode likelihoods, not facts.

Because of this, the model cannot store exact information. It can only store patterns.

Where patterns overlap, details are lost. Where details are lost, the model fills in the gaps.

That filling‑in is what we call hallucination. The many-to-one transformation also explains why rare facts vanish and plausible but false details appear.

A fluent answer is not necessaily a correct one. A fluent answer should not be over-trusted.

An LLM is not a database or lookup table. They are function approximators trained on vast data, forced to compress it into a limited parameter space (weights), and optimised for prediction, not truth.

Prompting is programming

Prompts act like programs for a probabilistic interpreter. And as they are written in natural language, prompts are prone to the mistakes that humans make in written instructions: ambiguity, no being explicit on what is required; not stating what is not required; and failing to mention who the output is for.

Structure beats style so that you can be sure your prompt acts more like a foundation for a robust interface, rather than one without structur built on shifting sand.

Constraints

Constraints beat persuasion. Constraining your LLM is essential. It is not about "being firm" with the model. It is about shaping the space of valid outputs so the model cannot wander.

In a prompt, when you say:

"Please answer carefully.”;"Try not to hallucinate.”;"Make sure you follow the instructions.”; "Be precise."

You are appealing to behaviour the model cannot guarantee, because persuasion relies on the model choosing to comply. "Please answer carefully" is a request. The LLM should "try not to hallucinate". What if it does? You have not said. This is like neglecting to define an else on an if.

Persuasion is weak because it competes with every other pattern the model has learned.

Constraints, by contrast, reshape the output space.

A constraint is something that reduces the degrees of freedom the model has when generating.

Examples of constraints are having the prompt specify that the LLM must output its result using a schema or specifying a role with explicit boundaries such as a 'user', 'system', or 'assistant' or by specifying the LLM "must cite X before Y".

Instead of trying to "convince" the model to behave, you damp down as close to zero as possible the possibility of misbehaviour.

Schemas beat prose. Treat prompts as code and debug them as code. Systems behave better when you design prompts as logic, not decoration.

Conclusions

Tokens drive model behaviour, so any dependable LLM system must be engineered around token‑level effects rather than surface text; the fragile parts of the stack are the retrieval, templates, guardrails, and data plumbing wrapped around the model, not the model itself; guardrails only become reliable when enforced by deterministic system logic instead of relying on the model’s cooperation; observability must reveal every transformation in the pipeline to make failures diagnosable; context windows function as short‑lived workspaces rather than any form of memory; retrieval quality has a larger impact on correctness than window size; hallucination is an unavoidable consequence of pattern compression and must be mitigated through system design rather than trust; and prompting only becomes stable when treated as programming with explicit constraints instead of attempts at persuasion.

The real interface: tokens, not text
The model is not the system
Determinism is a design choice
Temperature
Top-p
LLMs compress patterns, not facts
Prompting is programming
Constraints
Conclusions
Related Work
Table of Contents

Phroneses.com - build

Why Junior Engineers Matter More as AI Expands

The Adaptation of the Junior Engineer in an AI‑Accelerated Profession

The Changing Weight of the Work

AI Introduces New Types of Failure

The Organisational Obligation

Emerging Responsibilities

Failure‑Mode Literacy

Evaluating LLM output

Schema reliability

Instruction adherence

Grounding fidelity

Deterministic stability

Compliance and Safety

Creation vs Integration

The Apprenticeship Model Returns

A New Path to Seniority

The Cultural Shift

Practical First Steps for Juniors

Practical First Steps for Leaders

The Evolving Value of the Junior Engineer

Final Thoughts

Related Work

Table of Contents

Agents Cannot Maintain Systems: The Additive–Transformative Gap in LLM Software Delivery

The Promise of Automated Software Delivery

What the Labs Have Actually Delivered

Why is this?

Persistent state creates temporal dependencies

Writing code to Agentic Systems: The Fundamental Gap

Producing a PR‑ready diff (the section in question)

What can I do?

Why this matters: code is cheap, judgement is not

Final Thought

Related Work

Table of Contents

Further Reading

Team-Based AI Engineering is Next Step After Individual AI for Coding

Modern Software is delivered by Teams

What an Engineer Does

Software Engineer Adoption of AI is Individual

Accelerate One, Accelerate Many

Team Throughput

AI Acceleration

Example: How reduced waiting shortens the cycle

Without team level AI

Team level AI reduces waiting

Reducing idle time is key

AI Benefits at the Team Level

Why Team AI is Necessary

Team level AI

Individual AI cannot coordinate across roles

Team level uplift is a multiplier

Conclusion

Related Work

Table of Contents

Further Reading

Global AI Trends 2024–2025

Global Trends in AI

Global picture

Global adoption and diffusion

Commercial traction and investment

Conclusions

In the Future

North America

United States

Canada and Mexico

Mexico

Conclusions

Looking to the Future

Canada and Mexico

The United States

How U.S. businesses can build on their current position

Europe, Middle East and Africa

Europe

United Kingdom

Middle East

Africa

Conclusions

Looking to the Future