“We have full observability" is the most dangerous sentence in agent deployment. What you have is - Logs. Traces. Metrics. Dashboards.
NOT logs of the compounding reasoning errors that led to those actions. Visibility ≠ Understanding.
Our latest research paper introduces AgentCompass, a memory-augmented evaluation framework for post-deployment agent debugging without actually having to manually write or tune evals. It models the reasoning process of expert debuggers with:
🔹 A multi-stage pipeline (error identification → thematic clustering → quantitative scoring → strategic summarization)
🔹 A formal error taxonomy spanning reasoning, safety, workflow, tool, and reflection failures
🔹 Density-based clustering (HDBSCAN) to surface recurring failure modes across traces
🔹 Episodic + semantic memory for continual learning across executions
On the TRAIL benchmark, AgentCompass set a new state-of-the-art:
✅ Localization Accuracy: 0.657 (vs. 0.546 for Gemini-2.5-Pro)
✅ Joint score: 0.239 (highest reported)
✅ Uncovered safety and reasoning errors missed by human annotations
If you’re deploying AI agents at scale, don’t read this later. Read it now and tell us how it helps.
Read the full paper -> https://shorturl.at/844yb
Debug your AI with Compass in 5mins - https://shorturl.at/NP0VO
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