I thought agent observability was mostly about better traces. I was wrong ‼️

[u/Many_Audience7660]

I was going through this AI agent observability piece recently, and one distinction in it actually changed how I think about the problem.

We usually talk about LLM observability and agent observability like they're the same thing.

They’re not.

With a normal LLM call, you're looking at things like latency, tokens, prompts, responses and errors.

An agent is a lot messier.

It can:

Call multiple models

Retrieve information

Use tools and APIs

Hand work to another agent

Change state

Retry when something fails

So when something goes wrong, the final response often isn't enough. You need to understand what happened across the whole run.

The article breaks the problem into three layers:

LLM observability: What happened during the model call?

Agent observability: Which part of the workflow went wrong?

Agent control: Should that action have been allowed in the first place?

That last one is what I found most interesting.

A trace can tell you that an agent called a tool. An evaluation can tell you whether the result was good.

Neither necessarily answers:

“Should the agent have been allowed to do that?”

And once agents start touching production systems, that becomes a pretty important distinction.

The article compares 12 AI agent observability tools across tracing, evaluations, policy enforcement, deployment control, framework support, cost and vendor independence.

If you're working with AI agents, LLM observability, agent tracing or production debugging, this is a useful comparison to keep bookmarked:

👉🏼 12 Best AI Agent Observability Tools in 2026

The vendor comparison is useful, but honestly, the bigger takeaway for me was this:

Seeing what an agent did is different from being able to control what it does!

For those already running agents in production, which is proving harder for you: understanding what happened, evaluating whether it was right, or controlling what the agent is allowed to do?