TL;DR
Traditional APM tools (Datadog/New Relic) tell me about infra and API health, not why an agent chose the wrong tool or produced a bad answer. LLM observability platforms (LangSmith/Arize) expose traces, but I still had to manually review thousands of them.
Recently, I tried Agent Compass. It:
- Clusters similar failures so I debug categories, not one-off traces.
- Maps symptoms to likely root causes (retrieval drift, tool thresholds, prompt regressions, guardrail friction, etc.).
- Suggests actionable fixes** and lets me validate them quickly.
Below is my step-by-step flow, the checks I run, and the way I confirm the fix.
Why agents are hard to debug (the short version)
- Dynamic paths. Unlike classic request→response code, agents branch, call tools, and recover on the fly—creating thousands of traces with no obvious pattern.
- APM ≠ agent reasoning. APM helps me with latency, errors, and throughput, but it can’t tell me why the agent selected Tool A over Tool B or hallucinated a step.
- Trace viewers stop at presentation. They show me the data, but I’m still left to find patterns and hypothesize root causes manually.
Agent Compass adds the missing analysis layer.
What Agent Compass gives me
- Automatic error clustering. I get groups like “Incorrect currency in final response” or “Tool not invoked despite high-confidence match.”
- Symptom → cause suggestions. Each cluster comes with ranked hypotheses: prompt drift, retrieval index staleness, tool threshold miscalibration, guardrail block, context overflow, etc.
- Actionable fixes. It proposes concrete changes (prompt snippets, retrieval settings, tool thresholds, policy tweaks) and a one-click way to re-run an eval set to verify.
My checklist (I actually use this)
- [ ] Identify top cluster by impact
- [ ] Read 2–5 representative traces
- [ ] Pick one smallest, highest-leverage fix
- [ ] Re-run focused eval set
- [ ] Check adjacent clusters for regressions
- [ ] Commit or roll back
- [ ] Add a short note in the runbook (what/why/result)
FAQ I get asked
Q: Can’t I do this with plain trace viewers?
You can, but clustering + symptom→cause mapping removes most of the manual pattern-hunting. That’s where the hours go.
Q: What if the top fix doesn’t work?
I roll back, try the next hypothesis, and keep changes atomic so I know which one moved the needle.
Q: Will this hide rare edge cases?
No—clusters make the common case fast to fix. I still keep a “long tail” playlist I revisit weekly.
This flow turned “days of debugging” into a five-minute loop for me:
If you’re drowning in traces with no obvious pattern, try leading with clusters → hypotheses → tiny fix → quick eval. It’s the fastest way I know to turn agent behavior from a black box into a feedback loop.
Try Agent Compass:https://h1.nu/1iqW5
Read the science behind it all:https://h1.nu/1iqVJ
Integration Docs:https://h1.nu/1dFT5
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