Articles
Notes from shipping software with AI
For engineering leaders who want AI-assisted delivery they can check. Written from our own work, not from the sidelines.
Topics
- AI agents (13)
- AI Coding Agents (1)
- AI delivery (88)
- AI Delivery Programme (12)
- AI Workflow Audit (2)
- Architecture Review (2)
- Laravel (1)
- MVP to Production (1)
Start here
Review capacity
The review queue is the bottleneck AI coding created
Why more code did not mean more shipped, and where the queue actually forms.
Instruction drift
Agent instruction drift in enterprise AI teams
How every developer ends up with a different AI, and what a shared rule set changes.
What was tested
AI coding checkpoint contract: make checks reproducible
What “tested” should mean before a pull request is allowed to say it.
Latest
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When is a major refactor worth the risk?
A major refactor is worth it when a proven structural constraint blocks important work and migration can proceed in verified slices.
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How do you measure technical debt without fake precision?
Measure technical debt through repeated delivery cost, failure risk, manual work and ownership gaps tied to real roadmap decisions.
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How do you understand an undocumented codebase?
Map an undocumented codebase by tracing one real behaviour through entry points, data, effects, tests and ownership.
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Where should developer documentation live?
Put developer documentation beside what makes it true: repository facts with code, runbooks near services and policy with its owner.
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Why do AI coding agents say done too early?
AI coding agents stop at visible task signals. Define done with acceptance evidence, final-commit checks, review and release readiness.
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What breaks when teams run multiple coding agents?
Multiple coding agents create conflicts, stale context and review queues. Use bounded tasks, explicit ownership and controlled integration.