Know What Your AI Is Actually Doing in Production

Tracking retrieval recall, precision, and latency alongside vector store query volume and index performance. Retrieval failures account for most RAG underperformance in production, and tracing that only captures the model call misses this layer entirely.

Tracing the full execution graph across agent handoffs, tool, sub-agent, and skill calls, and retry loops, using OpenTelemetry-based instrumentation and open-source SDKs, not proprietary lock-in. A failed workflow shows exactly which step it started at.

Scoring production outputs for faithfulness, relevance, and hallucination rate continuously, not just at launch. Output quality drifts when prompts change, model providers update checkpoints, or real user queries shift away from what the evaluation set covered.

Continuous sampling of production outputs against retrieved context, with faithfulness scores below defined thresholds triggering alerts before quality degradation reaches a level users notice and report.

Automatically redacting PII and other sensitive data from logs and traces before they're stored, so personal data never sits in a debug log by accident, supporting GDPR, HIPAA, and SOC 2 requirements around data handling.

Per-request token consumption and cost tracking attributed by team, application, environment, or agent, with anomaly alerts when usage patterns shift and alerts when spend approaches defined thresholds.

Alerting when a model provider pushes a checkpoint update that changes output behavior or quality, so a silent upstream change doesn't become a downstream regression the team only discovers from user complaints.

Tamper-evident, append-only logging of every model decision and tool action, structured to satisfy compliance requirements under the EU AI Act Article 12, which mandates automatic event logging for high-risk AI systems, and SOC 2 audit expectations.