How do you verify AI agent outputs beyond unit tests and LLM-as-judge?
Practical_Green5775 · reddit · 2026-10-27
- Reddit poster researching AI agent testing in healthcare, where a technically successful run doesn't mean the output is correct.
- Failure modes that are hard to catch: valid-looking answers with unsupported conclusions; meaning drift in system-to-system mapping; workflows completing but violating key constraints; two eval methods disagreeing on the same output.
- Asks: which failures are hardest to catch pre-deployment? Custom evals vs LLM-as-judge vs deterministic checks vs manual review? Which output part would be most valuable to auto-verify?
- Context: building Orinyx, an open-source verification MCP for healthcare AI workflows (synthetic data), beta this Thursday, seeking early testers.
More from coding & agent
- UCSB CS Prof. Xifeng Wang lands 3 grants worth $180K+ for practical agentic AI research — xwang_lk · 2026-10-27
- Gen games don't need to be realtime — turn-based interactivity works, devs argue — djcows · 2026-10-27
- Player rebuilds Street Fighter II in HD with Claude: $3,000 of tokens, 3 weeks — Fantastic-Jeweler781 · 2026-10-27
- How to ace the "send us something cool" AI interview: research interviewers with agents, then focus — brandon_galang · 2026-10-27
- Give AI a clean personal-data-free machine with full control: agents running 24/7 teach you more than any course — EXM7777 · 2026-10-27
- 20 papers across 6 areas to master production-grade agentic AI engineering — MaryamMiradi · 2026-10-27