OpenTelemetry
If you are a backend engineer responsible for a production multi-agent LLM system in 2026, you have almost certainly hit the same wall: something broke in a pipeline that spans a planner agent, two tool-calling sub-agents, a retrieval step, and a final synthesis agent, and your observability stack told you
AI Agents
You just shipped your first agentic feature. Maybe it is a customer-facing AI assistant, an automated workflow engine, or a code-review bot that runs inside your SaaS product. Your agents are handling real user requests, tool calls are firing, LLM responses are streaming back, and everything looks fine in your
OpenTelemetry
I have enough context to write a thorough, expert-level post. Here it is: --- Picture this: your multi-agent AI pipeline just silently returned a wrong answer to a paying customer. Agent A called Agent B, which called a retrieval tool, which called an LLM, which hallucinated, which caused Agent C
OpenTelemetry
You've built an AI agent pipeline. It calls an LLM, maybe invokes a few tools, retrieves documents from a vector store, and chains reasoning steps together. It works, mostly. But when it doesn't, you have no idea why. The logs are a wall of JSON. The
Observability
You've spent years building systems you understood. You knew what a slow database query looked like. You knew how to read a flame graph. You knew that when your p99 latency spiked, you could open a trace, find the offending span, and fix it before your on-call rotation