Anthropic Engineer Shares 5 Steps to Build Self-Improving Agent Evals
alexcovo_eth · x · 2026-08-03
An Anthropic engineer points out that the biggest mistake in current AI development is building agent workflows without self-improving evaluation mechanisms, which once cost their team two years.
He shared a 5-step guide to building a self-improving eval system:
- Calibrate Initial Difficulty: Test with 50 real user prompts. Aim for a 50% pass rate (the sweet spot); anything above 80% is too easy.
- Automate Analysis: Feed failed run transcripts to the Haiku model to diagnose "what went wrong," building your eval set for free.
- Dual Scoring: Evaluate both the final answer AND the execution path. Right answer, wrong path means it will break eventually.
- Catch Fake Improvements: Re-run evals when plugging in a new model. Use transcripts to unmask false score bumps (e.g., the model dodging a bug).
- Integrate with CI: Plug evals into your CI/CD pipeline. No green evals = no deploy.
More from coding & agent
- Eve framework: batteries-included AI agent deployment with durable execution and sandbox — evilrabbit_ · 2026-08-03
- Decode: A Whiteboard for Reviewing and Guiding AI Coding Agents — Vjeux · 2026-08-03
- Prompting AI to Build a Zero-Asset Dungeon Crawler with Fluid Simulation — Dimillian · 2026-08-03
- OpenWiki 0.2.5 Introduces `.openwikiignore` to Save Tokens — LangChain · 2026-08-03
- AI Agent Architecture Evolution: Separating Tools from Brains Redefines the Computer — irvinebroque · 2026-08-03
- AgensWorks Introduces Visual Graph View with Custom Extension Support — jasonkneen · 2026-08-03