How Goose Uses SigNoz to Detect Silent AI Quality Failures in RAG Pipelines

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Developers behind Goose, built for the Agents of SigNoz hackathon, identified a critical blind spot in RAG chatbot monitoring: HTTP 200 responses can mask confidently wrong AI answers that standard metrics never flag. The team instrumented their RAG app with OpenTelemetry, adding custom quality attributes such as quality.score, entity_match, and tool.output_valid to each chat span alongside standard trace data. These signals feed into SigNoz dashboards that can show a sharp quality drop even while traffic and error-rate metrics remain healthy, a pattern the team calls the 'silent fail cliff.' When quality.score falls below a set threshold, an alert fires to a webhook that triggers Goose, an MCP-based agent that queries SigNoz for evidence, generates a markdown root-cause analysis, and can optionally auto-remediate the retrieval break. The project argues that semantic quality alerting deserves the same operational urgency as traditional uptime monitoring for teams shipping production RAG systems.

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