Developer Adds Observability to AI Rare Disease Pipeline Using SigNoz

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A developer building an AI platform to help patients with rare and undiagnosed diseases navigate specialist referrals in India instrumented their system using SigNoz ahead of the WeMakeDevs × SigNoz hackathon. The platform uses a LangGraph-based multi-agent architecture with twelve components, including agents for symptom intake, HPO extraction, clinical triage, and differential reasoning. The developer found that despite detailed design documentation, they had no real-time visibility into how agents performed or failed during actual pipeline execution. To address this, they self-hosted SigNoz and wired it into a two-service slice of the pipeline, exploring traces, metrics, logs, dashboards, and alerts. The exercise highlighted a key challenge in multi-agent systems: bugs rarely surface in the component being directly observed, making distributed tracing essential for root-cause analysis.

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