Give AI the Context It Should See, Not the Whole Repository
Many AI task failures do not happen because the model cannot modify code. They happen because the model reads the wrong context.
harness
7 public items tagged harness.
Evidence Contract: AI Delivery Must Come With Proof
Explains why AI delivery must include verifiable proof: tests, logs, screenshots, risk notes, and a review path, not only a claim that work is done.
From AI Failure to Project Memory
Shows how repeated AI mistakes should become project memory, updated rules, and regression checks so the delivery pipeline improves after each failure.
Put Humans at Risk Boundaries, Not Only at the Final Approval
Shows where humans should stand in an AI delivery pipeline: requirements, risk boundaries, release decisions, rollback choices, and final acceptance.
Stage-Gated AI Workers Need Isolated Workspaces
Explains why real projects should put AI work in isolated branches or workspaces, then move changes through explicit gates before they reach the main codebase.
What a Project-Specific AI Delivery Pipeline Means
Defines a project-specific AI delivery pipeline: AI acts as a worker while the project owns task intake, context, gates, evidence, and release boundaries.
From Chat Request to Task Contract: Route the Work Before AI Executes
The most common risk in AI-assisted development is not that the model cannot write code. It is that the model starts writing code too early.