Agent Evals Are Still a Whack-a-Mole Game
maksym_andr · x · 2026-07-13
The author notes that their current approach to benchmarks and evals feels like a temporary "whack-a-mole" mode: patching things up with a new test or rule every time an issue pops up.
While manageable at current capability levels, they warn this method will fall short as agents grow more powerful, necessitating more systematic evaluation and constraint designs.
More from coding & agent
- A roundup of AI agents and MCP resources, including how to evaluate agents — _jaydeepkarale · 2026-07-21
- A full course shows how to build and deploy an AI agent with OpenAI and LangChain — _jaydeepkarale · 2026-07-21
- A beginner guide to AI agents points readers to a Stanford webinar — _jaydeepkarale · 2026-07-21
- A practical guide on how to evaluate AI agents — _jaydeepkarale · 2026-07-21
- MCP is headed toward easier scale, event-driven extensions, and workable file uploads — EricBuess · 2026-07-21
- Developers debate the missing composition model for AI agents — threepointone · 2026-07-21