Kubernetes & Cloud Native · 04.08.2026, 15:18 UTC
You can’t debug what you can’t see — Observability for AI Agents
| Schweregrad | info |
|---|---|
| Kategorie | Kubernetes & Cloud Native |
| Quelle | CNCF ↗ |
| Veröffentlicht | 04.08.2026 UTC |
Sicherheitsmeldung mit Schweregrad noch nicht bewertet. Technische Details im Tab „Originaltext“; empfohlene Schritte in der Checkliste.
Traditional APM can’t tell you why your agent spent far more than usual asking the same question three times. We’ve been running AI agents in production for months. The hardest part isn’t building them, it’s understanding what they’re doing when they go wrong. Agents don’t crash with stack traces. They loop, hallucinate, burn tokens, and produce plausible-looking output that’s subtly wrong.
Here’s what we learned about seeing inside.
Why Standard Monitoring Falls Short
Standard application monitoring answers questions like:
Is the service up?
How fast are responses?
Are there errors?
Agent monitoring needs to answer different questions:
Why did this task cost dramatically more than usual?
Why did the agent call the same tool repeatedly?
Did the agent actually do what it said it did?
Which model is best for this task type?
Is the agent learning, or is it making the same mistakes?
These are fundamentally different questions. Prometheus counters and Grafana dashboards alone won’t answer them.
The Three Pillars for Agents
1. Traces — The Session Timeline
Every agent session should produce a trace — not a generic APM trace, but an agent trace that captures the full decision history: each model call, each tool invocation, each sub-agent delegation, with timing and cost attached.
We use Langfuse as our trace backend. Every LLM call, tool execution, and sub-agent delegation is a span. Traces nest — sub-agent work appears as children of the parent trace, so you can follow delegation without losing the thread.
Trace delivery must be non-blocking. Tool execution …