Traces don't tell the whole story: verifying what AI agents actually did
Ok-Machine7983 · reddit · 2026-11-05
A team building an agent observability system highlights the gap between what an agent says it did and what actually happened. Traditional traces show LLM calls, tool calls, inputs/outputs and execution flow, but not whether the intended business outcome occurred.
Their examples:
- Agent claims it booked a meeting — was the event actually on the right calendar?
- Claims it updated a CRM record — was the data really saved?
- Tool returned success — did the intended business action happen?
They've built a proxy-based system capturing agent interactions and are exploring linking those to external systems to verify outcomes. They ask practitioners: how do you verify completion today (tool responses, read-backs, manual review), and which failures are hardest to detect?
More from coding & agent
- Dev hands dead project to Claude Code: 24 autonomous experiments in 3 weeks, 100x traffic — doooyle · 2026-11-05
- RAG 101: One Untuned Setting Drops Answer Accuracy from 100% to 33% — dima806_dima · 2026-11-05
- ClankerCloud teases new agent-builder UX: simple vs workshop modes are hard to get right — tekbog · 2026-11-05
- Dev claims Codex limits feel unlimited with good context management — here's his setup — haider1 · 2026-11-05
- Founder runs daily agent standup on GStack: Office Hours hands off to CEO agent — garrytan · 2026-11-05
- Every talk: turn "this answer feels better" into tests for models and agents — every · 2026-11-05