Articles
AI delivery
How teams deliver software with AI they can check: specifications, reviews, tests, records of what was checked, and the workflow around coding agents.
- 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)
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What decisions should enterprise AI teams commit to the repository?
Commit decisions that change scope, risk, architecture, data use, or acceptance, and link them to the change they authorized.
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Why do enterprise AI teams need workflows instead of better prompts?
Prompts help a single interaction; a workflow defines the durable handoffs, evidence, authority, and stop conditions that many people and tools must share.
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What documentation should an enterprise create before using AI coding agents?
Document the decisions an agent cannot safely infer: module boundaries, approved commands, data rules, review owners, and release constraints.
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How do you stop AI test output from consuming the context window?
Keep the full log as an artifact, but return a compact failure summary to the agent: command, exit status, failing cases, first useful error,…