harness-engineering

To optimize CI/CD pipelines as agent feedback loops, you must structure tests and linters to provide immediate, automated error detection. This allows AI agents to parse output directly and self-correct during autonomous coding sessions.

The best way to enforce architectural boundaries is through mechanical enforcement using tooling like type systems and linters, rather than relying on human-readable instructions, ensuring AI agents reliably maintain project structure.

To make documentation machine-readable, you must structure it as an artifact that AI agents can act upon directly. This involves formatting data so autonomous coding workflows can parse context without human intervention.

You increase AI coding agent autonomy progressively by designing infrastructure that grants privileges based on demonstrated reliability, using robust test suites and CI pipelines to validate successful self-correction.

Automated linters and type systems are preferred over human-readable instructions because they provide mechanical enforcement of coding standards, creating direct, unambiguous feedback loops that AI agents can process autonomously.

No specific framework is required, but the test suite must be structured to provide immediate, automated error detection. Any framework works if its output acts as a direct feedback loop for the AI agent.