Every agent failure,
classified and fixable
Agent Compass automatically clusters production failures, classifies them against a 30+ error taxonomy, scores every trace on four axes, and recommends confidence-scored fixes - with zero configuration. Send traces, get answers.
Feed
Track, capture, and resolve errors from one place
Not just error tracking -
error intelligence
Traces with the same failure signature are grouped into clusters automatically - "Hallucinated refund policy ×47" instead of 47 individual alerts. Each cluster shows event count, first/last occurrence, and a trend graph so you see whether a problem is growing or shrinking. Click any cluster to drill into individual traces.
See the feed viewEvery error is classified against a comprehensive taxonomy - hallucinated content, ungrounded summary, wrong tool chosen, invalid tool params, PII leak, biased output, token exposure, goal drift, dropped context, missing CoT, and 20 more. Each classification includes evidence snippets from the LLM response, root causes, and affected spans.
Explore the taxonomyEvery trace is scored (0–5) on four axes: Factual Grounding (hallucination risk), Privacy & Safety (PII, credential leaks, unsafe advice), Instruction Adherence (format, tone, constraints), and Optimal Plan Execution (tool sequencing, workflow logic). Scores are clickable - drill into the taxonomy metrics that drove each score.
See scoring in actionAgent Compass doesn't just find errors - it recommends fixes. Every error includes an immediate fix (minimal patch to stop the bleeding) and a long-term recommendation (architectural change for a robust solution), both with confidence scores. Uses episodic memory from past runs and semantic memory from error patterns.
See how recommendations work Every failure classified,
every fix recommended
Catch hallucinations with evidence
Every hallucinated claim is flagged with the exact words that triggered it, the retrieval chunks that were available, and whether the agent fabricated content or used the wrong chunk. Clustered by topic so you fix the root cause, not individual symptoms.
Debug tool selection and parameter errors
See when your agent picks the wrong tool, passes invalid parameters, misinterprets tool output, or fails to call a tool it should have used. Each error shows the affected span, the tool call payload, and what the correct action would have been.
Surface PII leaks and security failures
Detect PII exposure, token leaks, credential exposure, insecure API usage, and biased output - classified under the Safety & Security taxonomy. Each incident includes evidence snippets and the exact span where the leak occurred.
Identify workflow and planning failures
Catch goal drift, step disorder, redundant steps, dropped context, and missing chain-of-thought - the subtle failures that don't throw errors but produce wrong answers. Agent Compass detects these through its Workflow & Task Gaps and Reflection Gaps taxonomy categories.
Prioritize by trend and severity
Each cluster shows event count, trend direction, and first/last occurrence. Errors scored on four axes (grounding, safety, instruction adherence, plan execution) so you fix the highest-impact problems first - not the noisiest ones.
Turn errors into test cases
Feed production error patterns back into simulation scenarios and evaluation datasets. Agent Compass learns from past runs using episodic and semantic memory - so the same failure pattern gets caught faster next time.
From trace to fix
with zero configuration
Send traces - zero config required
Agent Compass runs automatically on Observe projects. Send traces via OpenTelemetry or any supported SDK (Google ADK, OpenAI, LangChain, LlamaIndex). Set your sampling rate (1–100%) and Compass starts analyzing immediately - no eval config, no metric setup.
Errors cluster and score automatically
Traces are classified against 30+ error types, grouped into clusters by failure signature, and scored on four axes. Each cluster shows event count, trend graph, evidence snippets, root causes, and affected spans. New traces join existing clusters or create new ones in real time.
Apply fixes with confidence scores
Every error includes an immediate fix and a long-term recommendation, both with confidence scores. Drill into any trace to see the full execution - input, retrieval, generation, tool calls - and pinpoint the exact span where things went wrong. Feed patterns back into simulations to verify the fix.
Powering teams from
prototype to production
From ambitious startups to global enterprises, teams trust Future AGI to ship AI agents confidently.