AI agents are powerful. They reason, adapt

and can act all on their own. And they can create tremendous value for a range of different use

cases like customer service, supply chain, IT operations and many other

tasks. But here's the problem. In production, they can go rogue. Think about it.

An AI agent could make a decision that you can't explain to where you

wouldn't be able to trace the inputs to the outputs.

Or, you could have multiple outputs for the same input and not be

sure of which one is correct. Or worse, it could fail silently in

between and you would not be able to tell where it happened. When that happens,

debugging is almost impossible. Compliance is at risk and most importantly, both

reliability and trust can erode.

In practice, observability for AI agents rests on three key pillars.

First is decision tracing, understanding how the agent came to

decisions to get from the input and output in all of the steps that it took in between. Second

is behavioral monitoring, understanding what the the agent was inferring. Were there any loops

or anomalies that we need to be aware of or other risky patterns? Third is outcome

alignment, starting with get input and context. Did it actually generate the outcome

that was intended? Together, these three things give us transparency,

visibility and operational control. So how does this actually work? It starts with

capturing three types of information. We talked about the inputs in context, basically

the instructions that the agent was given and the initial information that are received. Then we

move on to the decision and reasoning, understanding the thinking that's happening

within the agent to drive towards those actions and results. And then finally, the outcome in

ensuring that it actually matched the intent of what the agents started with. All of these pieces

of information get logged as structured events to understand the behavior

and patterns of the agent. Together, we stitch them together like a timeline to

understand what the agent did, and we can use it like a replay to be able to go back

and understand the behavior and see whether there's anything we need to change. And again,

checking whether the outcome matched the original input and intent. Did the agent

stay aligned with what we wanted it to do, or did we see anomalies? This is where

observability differs from monitoring. Whereas with monitoring you have the raw signals

like the CPU load or the token count or error rates.

With observability, you actually have the the context of the decision trail, being

able to trace everything that was done and be able to analyze that replay and

improve the agent's behavior going forward. So here's the takeaway. Observability for AI agents

isn't just dashboards or metrics. It's a full picture of the inputs,

the decisions that the agent took and the outcomes.

With those three things together, stitched into the timeline that we have, we can understand

what the agent did, why it did it and build that transparent trail that you can

trust, analyze and ultimately improve. That's what makes it possible to operate autonomous

systems reliably at scale.