Insights
Field notes on the future of software delivery.
Not a blog but a body of work. Each cluster starts with a pillar idea, builds through supporting pieces, and resolves into the framework that operationalises it.
Agentic Engineering
Moving teams beyond vibe coding to disciplined, agent-assisted delivery. Operationalised by the The AI Engineering Maturity Model framework.
What Is Agentic Engineering?
Once generation becomes abundant, the constraint moves to acceptance. That single shift reorganises how software gets built.
AI Engineering · 6 minBeyond Vibe Coding
Vibe coding optimises the step that just became abundant and starves the one that just became scarce. Fine for exploration. Dangerous as an operating model.
AI Engineering · 6 minThe Agentic SDLC Is an Acceptance-Gate Problem
AI now reaches across the whole lifecycle, not just the IDE. The design question is no longer where agents act, but where a human must accept, and what evidence the agent owes them at each gate.
AI Engineering · 7 minAgentic Code Review
Reviewing AI code is not reviewing human code. The author cannot tell you where it was unsure, so the gate that closes the Acceptance Gap needs a different checklist, not the old one applied faster.
AI Engineering · 6 minContext Engineering: Context Is the New Architecture
Most context-engineering advice optimises the ephemeral window. The durable asset is the context layer you own and version: the conventions, decisions and contracts that let a generated change be judged correct, fast.
AI Engineering · 7 minThe Memory Hierarchy of an Agentic Team: Choosing Between Convention Files, Retrieval, Graphs and Fine-Tuning
Context engineering has quietly become a systems-design problem with four competing substrates. Stop picking one by default; design a memory hierarchy with eviction, freshness and provenance rules.
AI Engineering · 6 minChoosing Models for Engineering Teams
The leaderboard is the wrong place to start. Once code generation is abundant, the model is a commodity input and the harness is the product, so model choice rarely closes the acceptance gap. Choose by use case, cost-at-acceptance, verified context and the data boundary instead.
AI Engineering · 9 minThe AI-Native Engineering Team
Once acceptance is the constraint, the org chart is wrong. Four judgement roles must be owned on every team — and 'more humans' now hurts.
Platform · 7 minEnforcing Determinism in Probabilistic Systems
Validation gates that make agentic output safe to ship.
AI Engineering · 7 minThe Eval Is the Spec: Why Acceptance Criteria Become Executable Tests in Agentic Delivery
When agents generate code for free, the prompt stops being the binding artefact. The evaluation harness becomes the real specification - and the team that writes the best evals, not the best prompts, controls quality and velocity.
AI Engineering · 8 minThe Integration Seam Is Where AI-Generated Software Breaks: Payments, Identity and the Limits of Generation
AI agents write clean code inside a service and confident nonsense at the boundary between systems. Idempotency, payment state machines, token refresh races and eventual consistency live in vendor quirks and production incidents, not in training data — and that is exactly where acceptance must now concentrate.
Architecture · 6 minEvent Contracts as the Coordination Layer for Mixed Human and Agent Teams
When some of the work is done by people and some by autonomous agents, the durable coordination mechanism is not the org chart or the ticket. It is the versioned, well-owned event contract — and schema governance quietly becomes agent governance.
AI Engineering · 9 minPrivate by Architecture: Running Local and Self-Hosted Models When Code, Payments or Identity Data Cannot Leave
For teams handling cardholder data, PII or proprietary source under regulatory constraint, the model-selection question is not which frontier model is smartest but where inference can legally and architecturally run. A deliberate tier of local and self-hosted models — with the capability gap closed by tooling, retrieval and orchestration rather than raw model quality — is now a practical engineering choice, not a compromise. Design the data boundary first, then place models inside it.
AI Engineering · 6 minMeasuring AI Engineering Properly
Lines of code and "percent faster" were always vanity metrics. Now they are actively misleading. If generation is abundant, measure the scarce step: acceptance, rework and stability.
Trust, Governance & the Economics of AI
How machine-produced work earns the right to ship — and what it costs. Operationalised by the The Governance-to-Value Ratio framework.
Designing the Human Acceptance Loop: Where People Must Stay in the System as Agents Take Over the Build
As agents absorb the build, the decisive human work shrinks to a few acceptance moments. Keep humans in every loop and you lose the leverage; remove them and you lose accountability. The discipline is to design the loop on purpose.
AI Engineering · 7 minAI Coding Governance That Enables, Not Forbids
Most AI coding policies are written to stop something, and they fail. The governance worth building does the opposite: it widens safe adoption while raising acceptance on evidence, not enthusiasm.
Delivery Assurance · 6 minProvenance Engineering: Reconstructing Who Decided What When Humans, Agents and Models All Contributed
When software is co-produced by humans, agents and models, "who decided this, on what basis, and can we reconstruct it?" stops being a forensic luxury and becomes a first-class engineering requirement.
AI Economics · 7 minThe AI Delivery P&L: Why Generation Got Cheap and Your Cost Base Did Not
Boards modelling AI savings on developer headcount are budgeting the cheapest part of the problem. When generation costs nothing, the bill moves downstream — to acceptance, integration, review and ownership — and the evidence says it gets bigger.
AI Economics · 8 minBuild, Buy or Generate: How Agentic Delivery Rewrote the Oldest Capital-Allocation Decision
Agentic generation collapses the cost of building — but it does not tilt build-versus-buy toward build. It dissolves the decision's premises and adds a third path with its own ownership liability.
Product Delivery · 7 minDelivery Telemetry: Instrumenting the Path from Intent to Production So You Can See Where It Stalls
Organisations instrument their running systems to the millisecond yet leave the journey from business intent to deployed outcome almost entirely un-observed. Once AI accelerates the build, the constraint moves upstream into decision and acceptance, and only instrumentation will show you where it went.
Delivery Architecture
Preserving intent from strategy through product to running outcomes. Operationalised by the Delivery Architecture: The Translation Layer framework.
The Missing Architecture Layer Between Strategy and Delivery
Strategy and delivery both have owners. The translation between them — capability maps, domain models, target and transition states — has none. Name it, and call it Delivery Architecture.
Product Delivery · 6 minWhy Most Product Transformations Fail Before Engineering Starts
The failure is decided before the first sprint is planned. By the time engineering inherits the work, the objectives are already ambitions, discovery has been treated as a phase to clear, and stakeholders are aligned on slogans rather than trade-offs. A team that builds fast simply reaches the wrong destination sooner.
Product Delivery · 7 minProduct Delivery in the Age of AI
AI now touches discovery, requirements, planning, build, test and docs. It compresses execution everywhere — and improves intent-translation nowhere. When generation is cheap, acceptance becomes the constraint.
Product Delivery · 7 minFrom Idea to Production: A Practical Product Delivery Lifecycle
Most delivery teams pour their attention into Build — the one stage the evidence says is least correlated with success. A practical, nine-stage lifecycle for shipping value, not volume.
Product Delivery · 7 minMVPs, Pilots and Production Systems: Knowing the Difference
Prototype, MVP, pilot and production are not sizes of the same thing — they are four different questions, each with its own acceptance bar. Confusing them causes over- and under-engineering, which are the same mistake.
Product Delivery · 7 minMeasuring Product Delivery: Beyond Velocity and Story Points
Velocity and story points measure how busy engineers are, not whether business intent became a sustained outcome. A field guide to building a delivery scorecard as a translation audit, where speed is never celebrated without acceptance.
Product Delivery · 8 minProduct Delivery Governance Without Bureaucracy
Heavyweight approval boards slow delivery without making it safer. The fix is not less governance but governance designed around decision quality: separate the irreversible decisions that deserve scrutiny from the reversible ones that just need to ship.
Product Delivery · 8 minBuilding Product Teams That Scale
Scaling a product team is an intent-translation problem, not a headcount problem. Why the org chart is a lagging artefact, and a decision-ownership migration map for the 10-30-50-150 thresholds.
Product Delivery · 7 minWhy Alignment Beats Agile: Product, Architecture and Engineering
Most teams are Agile. Few are aligned. Product describes one system, architecture models a second, engineering ships a third. The cadence improved; the seams stayed un-owned. Alignment is the real constraint.
Architecture & Decisions
Architecture as decision quality and velocity, not diagrams. Operationalised by the Decision Architecture framework.
Architecture in the Age of AI
When agents become co-authors, architecture stops being about diagrams and becomes the discipline of decisions that let an organisation accept changes it did not write by hand.
Architecture · 7 minArchitecture Is Not About Technology — It Is About Decision-Making
Diagrams and stack choices are the visible residue of architecture, not the work itself. The work is making trade-offs under uncertainty — choosing constraints and accepting risk for a specific context with incomplete information. The expensive failures are not bad pictures; they are good decisions nobody recorded and poor ones nobody could reverse.
Architecture · 7 minArchitecture Decision Records: The Missing Link Between Architecture and Delivery
Teams document what they built, not why. The rationale — the rejected options and the trade-off accepted — is the actual deliverable, and increasingly the context layer your AI agents are missing.
Architecture · 8 minWhy Most Architecture Functions Fail to Create Business Value
Architecture teams drift into review boards, document factories and gatekeepers. Their actual job is the opposite: reduce uncertainty, accelerate delivery and enable decisions. The evidence on why the drift destroys value is now hard to ignore.
Architecture · 7 minEvent-Driven Architecture Beyond the Technology
The broker was never the hard part. Once you have chosen Kafka or a queue, the difficult ninety per cent remains: who owns the event, what it means, and who is paged when it silently lands in a dead-letter queue.
Architecture · 7 minArchitecture Governance Without Bureaucracy
Heavyweight approval boards are empirically weak and quietly expensive. The alternative is not chaos — it is decision records, an advice process, and governance tiered by consequence rather than hierarchy.
Architecture · 7 minDesigning for Change: The Most Underrated Architecture Principle
Great architecture is not optimised for today's requirements, the one set you already understand. It is optimised for the changes you cannot yet name. But designing for change does not mean maximising flexibility everywhere; that is its own failure mode. It means deciding which changes you will make cheap to absorb, which decisions are genuine one-way doors, and engineering reversibility into the rest.
Architecture · 8 minArchitecture as a Product: Designing Platforms People Actually Use
Internal platforms fail when they are designed for the people who govern them rather than the people who use them. The smoking gun is a perception gap: producers are convinced a mandatory platform works while consumers are split. Adoption, freely given, is the only honest metric, and a mandate is an admission the architecture could not win it.
Product Delivery · 8 minThe Architecture Decisions That Determine Product Success
Build vs buy, platform, integration, scalability, domain boundaries — five 'technical' choices that are really bets on what the business will become. Architecture is where intent survives translation, or quietly dies.
Architecture · 7 minThe Architecture Decisions That Matter Most in Enterprise Transformation
Build/buy, platform versus product, integration, identity, and data ownership are not procurement line items. They are the one-way doors that decide whether a transformation succeeds before delivery even begins.
More insights
Platform Engineering for Agentic Teams
When AI agents become contributors, the internal platform stops being plumbing and becomes the control plane that decides whether AI compounds your strengths or your dysfunction.
Platform · 9 minGolden Paths: Preserving Intent at Platform Scale
A golden path is an architecture decision that only has to be made once — and, in the agent era, the only scalable way to give every contributor, human or not, the same intent.
Platform · 9 minCognitive Load, Not Feature Count
The best thing a platform — or an AI — can do is let a good engineer not think about something. That, not features shipped or lines generated, is the number worth chasing.
AI Engineering · 12 minFrom Tasks to Roles
Agents execute tasks. People hold roles. They are not the same unit — and confusing them is the quiet mistake under most AI-engineering failure. A three-layer model for what actually changes, and the part of the work that has a floor.