harness-evolution

Harness run continuous improvement works by automating post-run evaluation to score performance, mining run artifacts for generalizable lessons, and routing those lessons for safe change implementation, which ensures upgrades and proposals evolve the harness durably over time.

The best way to extract lessons from run artifacts is to automate lesson mining during post-run evaluation. This classifies generalizable lessons from harness outcomes to prevent recurrence and safely routes them for low-risk upgrades or significant change proposals.

Yes, you can safely apply low-risk upgrades while retaining knowledge by using durable memory. This mechanism records every lesson and applied change, ensuring the harness evolves continuously without losing historical knowledge or requiring manual tracking.

Yes, harness evolution operates without external dependencies for change management. It independently analyzes run artifacts, classifies lessons, and implements safe changes by utilizing its internal scripts and references to route improvements.

You should run post-run evaluation immediately after any harness run to identify areas for improvement. This timing captures accurate run artifacts and outcomes, allowing you to score effectiveness and route lessons for future iterations.