Operations | Monitoring | ITSM | DevOps | Cloud

Debugging AI Agents in Production Without Losing Your Mind

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.

What is OTLP and How It Works Behind the Scenes

If you have worked with observability tools in the last decade, you have likely managed, and been burnt by, a fragmented collection of tools and libraries. Each observability signal required its own tool, data formats were incompatible and had little or no correlation. For example, log records would not link to traces, meaning you had to guess which traces led to which events. The OpenTelemetry Protocol (OTLP) solves this by decoupling how telemetry is generated from where it is analyzed.

OpenTelemetry Collector Contrib - A Hands-on Guide

As application systems grow more complex, it becomes ever more important to understand how services interact across distributed systems. Observability sheds light on the behavior of instrumented applications and the infrastructure they run on. This enables engineering teams to gain better track system health and prevent critical failures. OpenTelemetry (OTel) has standardized how we generate and transmit telemetry, and the OpenTelemetry Collector is the engine that processes and export this data.