Agent optimization: Tuning Agents for Efficiency, Safety, and Reliability

Agent optimization focuses on tuning AI agents for speed, cost, and reliability. Agent optimization includes prompt tuning, caching, limiting context windows, and choosing the right model size. Combine agent optimization with LLM evaluation to measure trade-offs.

Agent optimization also means setting policies for tool usage and retries, and implementing rate limits. Use agent optimization to reduce token costs while maintaining output quality. Monitor agent optimization metrics with LLM observability and iterate.

Link: https://github.com/future-agi/ai-evaluation