Thought Leadership
2026-03-145 min read

The Problem with AI Agent Observability

AI agents are the new software — but you can't see what they're doing. Here's why that matters.

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The Visibility Problem

Traditional software gives you observability for free. Logs, metrics, traces — the tooling is mature and well-understood.

AI agents are different. You fire off a prompt, the agent reasons, calls tools, makes decisions — and you get back a result. What happened in between? You don't know.

Why This Matters

When your agent breaks in production, you need answers:

  • What did it do?
  • What did it cost?
  • Why did it take so long?
  • Where did it go wrong?
  • Without visibility, you're debugging blind. You're shipping code you can't inspect. And you're spending money you can't account for.

    The State of Tooling

    There are observability tools out there. Some are good. But most only show you part of the picture — an LLM call here, a token count there. They weren't built for the way agents actually work: multi-step, multi-tool, with decisions at every turn.

    The tooling hasn't caught up to how teams actually build agents.

    What Good Observability Looks Like

    We think agent observability should be:

  • Automatic — Add a few lines of code and everything is captured
  • Complete — Traces, costs, errors, latency — not just LLM calls
  • Useful — Help you debug, not just collect data
  • Actionable — Surface problems before users do
  • The Future

    Every agent should be observable by default. Debugging agents should be as straightforward as debugging any other software. That's the bar, and the industry isn't there yet.

    That's what we're working on.

    Ready to put this into practice?

    Start tracing your AI agents in 5 minutes with Trefur Observe.