An agent skill for eval-driven development of LLM-powered app
Agent-native eval workflow beats LangSmith's manual dashboard setup.

Automated rollback on regression is a killer feature LangSmith doesn't have.
Engineering teams deploying AI agents or LLM applications to production
LangSmith · Arize Phoenix · Helicone
Agent-native eval workflow beats LangSmith's manual dashboard setup.
Replaces stitching Langfuse and promptfoo together with one unified eval dashboard.
Subsumption Architecture revival cuts LLM calls with pattern cache misses.
Replays agent traces step-by-step to pinpoint exact failure turns automatically.
Machine-parseable traces for LLM agents when pdb and breakpoint() are useless.
Iteratively improves agent harnesses from 67% to 87% on tau-bench using production traces.