Once you build and deploy an AI agent, you will quickly realize that it needs babysitting. Why? Because agents are complex, expensive, unpredictable, and when something goes wrong, it is very important to know why.

This is where testing and monitoring comes in. Let’s break it down and see what to actually look at when you’re trying to keep your agent in line.

Observability

Observability is all about understanding what the agent is doing. It’s not just about whether it works or not, it’s about seeing every step, every tool call, every LLM decision, and every user reaction.

This inludes:

• Logs
• Metrics
• Tool usage
• Model calls
• Responses

This lets you monitor:

• Costs - if one user query triggers 5 unnecessary calls, that’s a red flag
• Latency - if your agent take 15 seconds because it waited on a slow external API, that can be an issue
• Harmful language or prompt injection
• User feedback

In short, you need to know what your agent is doing behind the scenes.

Tools

Tools like Langfuse, Arize, and Helicone help track what’s going on in the backend. They provide two main things:

• Traces - a full picture of the agent doing a task -Example: A trace might show the sequence of actions taken to book a flight; user input -> LLM call -> tool call to Skyscanner API -> response formatting

• Spans - individual steps inside that trace -Example: Within the above trace, spans would include the LLM call that extracted destination and dates, and the tool call that fetched flight options.

Monitoring

Here are the metrics to be monitored:

• Latency
• Cost
• Request errors
• User feedback
• Implicit feedback
• Accuracy
• Evaluation scores

Online vs Offline Evaluation

• Offline evaluation happens before you deploy. You test scenarios, run static evaluations, maybe use golden test cases Example: Simulate a user asking for travel plans to Vietnam and see if your agent suggests appropriate places

• Online evaluation happens in the real world, monitoring live interactions and outcomes Example: In production, users are dropping off halfway through the booking flow—your trace shows tool call failures on mobile browsers

Think of it like a game: offline is your training mode; online is when you go live and see if you survive the boss battle. Hence, you need both.

Why This All Matters

Observing and evaluating your agent is crucial for:

• Debugging
• Cost control
• Iterating and improving reliability
• Understanding what is breaking, where, and why