Your Agent Traces Are Training Data: 4 Ways to Turn Observability Into Post-Training Fuel
BraceSproul · x · 2026-10-18
A widely shared thread argues that massive volumes of agent traces ingested by observability systems like LangSmith are becoming fuel for continual agent improvement. Unlike traditional traces, agent traces are larger, far more information-dense, and aimed at improving the agent rather than debugging latency.
The author outlines two paths—harness engineering (e.g., Engine) and post-training. Traces feed post-training in four ways:
- SFT examples: successful production trajectories can teach a base model
- Tasks: real production agent inputs become eval/RL tasks
- Verifiers: trace feedback from users and other sources helps define success criteria
- Tool snapshotting: traces plus source code can reconstruct tools and state to build agent environments
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