self-improving-agent-skills

Automated prompt optimization for agent skills is handled by a multi-agent workflow that executes test scenarios, diagnoses failures, and applies targeted mutations to improve performance against binary evaluation criteria.

The best way to improve agent performance using Google ADK is deploying an execute-diagnose-mutate loop that generates test cases, analyzes failures, and applies evidence-based mutations to skill definitions until target pass rates are reached.

Yes, automated skill optimization works with FastAPI and uvicorn, which are required dependencies alongside Google ADK and Gemini API access to coordinate the Executor, Analyst, and Mutator agents.

To iteratively evaluate and refine agent prompts, the system targets existing skill folders, runs them against generated test scenarios, and keeps only those prompt mutations that demonstrably improve evaluation scores.

Missing security vulnerabilities during prompt evaluation often stems from imprecise prompt definitions, which the Analyst agent identifies by diagnosing root causes of failures and instructing the Mutator agent to apply surgical edits for better detection accuracy.

Yes, you need Gemini API access to run multi-agent skill evaluation, as the Executor, Analyst, and Mutator agents require both Google ADK and Gemini API access to coordinate the automated optimization loop.