Agent evaluation & engineering patterns
What is Agent evaluation & engineering patterns?
Developers are establishing standardized methods for testing, measuring, and improving AI agents—moving from ad-hoc prompting toward reproducible engineering practices with clear metrics and evaluation frameworks.
As AI agents move into production, the field is crystallizing around evaluation patterns and best practices that determine whether agents are actually reliable enough to deploy, which is becoming table-stakes for real-world adoption.
References
- How Do You Know Your AI Agent Got Better? — Medium: AI Agents
- Ouroboros: A coding agent that evolves its own harness and tops agent benchmarks — Hacker News
- Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy — ArXiv
- How LLMs Actually Work — The Foundation of AI Agents — YouTube
- Turning agent decisions into tested, deterministic code: Temper-Skills — Medium: LLM
- AgentTrajectorySentinel (ATS) — Real-Time Detection & Repair of LLM Agent Failures — YouTube
- From Million-Token Agents to Eval-Ready Data — Medium: AI Agents
- Open Source, third-party auditing for AI Agents — Hacker News