Operations | Monitoring | ITSM | DevOps | Cloud

AI agents are powerful, but debugging them in production is hard. Non-deterministic behavior, LLM latency, and token costs create observability challenges that traditional monitoring tools don't address. In this webinar, engineers from Inkeep and SigNoz walk through how Inkeep monitors its AI agent framework in production using OpenTelemetry-native observability.

Using SigNoz MCP Server & Claude to find root cause of Alerts.

Using SigNoz MCP Server & Claude to find root cause of Alerts.

SigNoz Demo Video - Interactive Dashboards and Correlation.

Langchain Agent Monitoring with SigNoz.

Alert RCA with SigNoz MCP Server & Claude.

LLM observability is broken There are too many new libraries floating around, but they don't follow accurately the OpenTelemetry conventions. OTel isn’t perfect for LLMs yet—but extending a proven standard beats inventing another one. Why not use the same standard (OTel) which works so well for rest of the apps, and just work on top of it? This is what I was ranting with Pranav Raj S, co-founder at Chatwoot and we thought there must be other folks facing similar issues.

Take control of your observability spending with complete transparency into usage patterns across logs, metrics, and traces. No more surprise bills or blind cost optimization - get the visibility you need to manage budgets effectively.

Take control of your observability spending with complete transparency into usage patterns across logs, metrics, and traces. No more surprise bills or blind cost optimization - get the visibility you need to manage budgets effectively.

Self-hosting SigNoz just got significantly easier with community-focused improvements that remove deployment friction and give you more flexibility in how you run your observability stack.