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We instrumented an AI agent swarm with SigNoz, and its own telemetry told us we were wrong about almost everything
TL;DR: Observability revealed that our own design assumptions were often wrong, as live telemetry contradicted our beliefs about system behavior. The project used SigNoz to track every step of a five-model swarm, catching issues that code reviews missed.
We instrumented a five-model AI swarm with OpenTelemetry via SigNoz, embedding traces for every action from planning to delivery. Our dashboards and traces exposed misconfigurations, outages attribution, and weak links we hadn’t seen in code reviews. Telemetry disproved assumptions about limits, reliability, and design quality, forcing us to rethink routing, review processes, and even the core design system. The live metrics drove changes they wouldn’t have surfaced from static code analysis.
Question for the room: What is your experience using end-to-end tracing to validate assumptions in AI agent coordination systems?
— via dev.to
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