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AI Reliability / EvaluationR&D and product architecture

Agent TraceReliability Intelligence

CLUSTERFAILURE SHAPES RAW TRACES → SIGNAL → RCA → RELEASE GATE
My roleAI Reliability Architect
ContextProduction agent systems
System classAI Reliability / Evaluation
FocusFailure patterns and release decisions
Brief

The problem

An agent can be technically available and still be functionally wrong. Raw traces expose spans but rarely reveal recurring retrieval, tool-output, planning, or fallback failures.

Constraints

Signals need interpretation

01Signals combine infrastructure, application, LLM, and agent-semantic telemetry.
02Failures are non-deterministic and often require comparison across many traces.
03The output must help engineers decide what to fix or whether a release should proceed.
04Evaluation costs and latency must remain bounded enough for continuous operation.
Architecture

From telemetry to release decisions

The system turns raw traces into release decisions by separating recurring failure patterns from isolated events.

Agent Trace / logical architectureSanitized view
01IngestOTel + agent traces
02NormalizeEvents, tools, retrieval, costs
03ScanSpecialized evaluators
04ClusterRecurring failure shapes
05ActRCA, alert, release gate
Decisions

How we evaluate

01

Use dedicated scanners

Grounding, retrieval, tool behavior, policy, latency, and cost fail differently and need separate evidence.

02

Cluster traces before human review

Compression turns thousands of spans into a smaller set of recurring operational problems.

03

Connect evaluation to deployment policy

Quality, cost, and latency regressions inform a release policy that can block or canary a release.

04

Preserve the original trace as evidence

Every cluster and automated RCA links back to source spans so engineers can verify the conclusion.

Production

Production signals

01Semantic SLOs for groundedness, tool success, and agent completion.
02Golden datasets and regression runs in CI/CD.
03Online sampling, drift detection, and incident thresholds.
04Cost attribution by model, workflow, and tenant.
05Runbooks for hallucination spikes, retrieval failures, and provider degradation.
OpenTelemetryLangfuseOpik conceptsLLM evaluationTrace analysisFailure clusteringRCASLOsQuality gatesAI incident managementCost telemetryAI SRE
Outcome

Recurring trace patterns become evidence engineers can use to diagnose regressions and decide whether to release.

Relevant problem?

Making agent quality visible enough to govern releases?

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