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filtered by #engineering clear
I Built an AI Harness Because I Want to Trust AI (Series Final)
Series Final. The six mechanisms covered across this series — knowledge graph, Auto Review, Self-Healing, Recurrence Prevention, non-engineer PRs — all hang off a single conviction: I don't trust AI to fill in the blanks for me. Not distrust of generation quality, but the clear-eyed acceptance that AI has no idea what context wasn't handed to it, and that 'ideal behavior with no spec given' is a fantasy. The starting point goes back to 2025, when I was trying to figure out how to make AI actually understand a large codebase — and ran into walls on both context window scaling (lost in the middle, attention dilution) and learning-based approaches (machine unlearning, destructive interference). GraphRAG + MCP became the way out: hand AI only the facts it needs, when it needs them, so it doesn't have to infer. From code-graph (which I burned two months on and threw away) to the current product-graph (cpg). This piece is the philosophy and the trial-and-error behind the whole series: harnesses confine where hallucinations are allowed to happen, design is translating principles into your own use cases, and Coverage 90% as a solo target breaks the implementation.
The Author Doesn't Have to Be an Engineer: How the Harness Holds Quality (Series Part 5)
Series Part 5. With the harness handling quality at the gate, the people closest to the requirements -- business-side managers, PMOs -- now open PRs to production directly, no engineer in between. Two recent examples (a deep root-cause fix and a +1,742 line feature build), the boundary of what they can and can't take on (anything on top of an existing stack vs. standing up new infrastructure), why it holds (the four mechanisms from Parts 1-4), and how the pattern carries over to consumer-facing services.