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
- ExplorationBench: Measuring AI Systems' Exploration in Verifiable Alien Worlds — ArXiv
- When Fast Thinking Meets AI: What Jev Teaches Us About Building Better Agents — Medium: LLM
- Designing Evals for AI Agents: Why "It Passed" Isn't Always "It Works" — Medium: LLM
- CheatBench: Measuring Reward Gaming in AI Agents — Hacker News
- AssBench is all You need to benchmark LLM Harness Intelligence /s — Hacker News
- SWE-Serve: Benchmarking Agentic Engineering For Production Inference Serving — ArXiv
- techexplorersg/llm-evaluation-gateway — Model-agnostic LLM evaluation gateway reference implementation for comparing providers, measuring quality, latency and cost, detecting regressions, and enforcing release gates. — GitHub
- OSWorld-Pro: Process-based Evaluation for Computer Use Agents — ArXiv