Observability is your ability to understand what's happening inside a system by looking at the data it produces.

In business automation, observability means you can see what your AI agents are doing, why they made specific decisions, and where problems occur. For example, if your accounts payable agent rejects an invoice, observability shows you the exact invoice data it reviewed, which rule triggered the rejection, and what information was missing. You're not left guessing why something happened.

Unlike traditional software monitoring that just tells you "system up" or "system down," observability shows you the full story.

It's the difference between your check engine light coming on (monitoring) versus seeing a diagnostic report that says "cylinder 3 misfiring due to faulty spark plug" (observability).

For finance teams running AI automation, this means you can trace any transaction from start to finish, understand exceptions before they become problems, and prove to auditors exactly how decisions were made.

Frequently Asked Questions

What's the difference between observability and monitoring?

Monitoring tells you that something is wrong. Observability tells you why it's wrong and how to fix it. Traditional monitoring might alert you that invoice processing slowed down.

Observability shows you that 47 invoices are stuck because a vendor changed their address format, your agent flagged them for review, and they're waiting in your approval queue.

Monitoring is like knowing your car broke down. Observability is like having the mechanic's full diagnostic report showing exactly which part failed and why.

Why does observability matter for AI agents more than regular software?

AI agents make decisions based on context and patterns, not just fixed rules. When a traditional automation script fails, you know exactly where in the code it broke.

When an AI agent makes an unexpected decision, you need to see the reasoning behind it. For example, if your procurement agent chooses a different supplier than expected, observability shows you that it found better pricing, factored in delivery times, and checked that supplier's reliability score.

Without observability, you just see the output and wonder why the agent made that choice.

What should I be able to see with good observability?

You should see the inputs your agent received, the decisions it made, the actions it took, and the results it produced.

For an accounts payable agent, that means seeing the invoice PDF that it processed, the data it extracted, how it matched line items to purchase orders, which approval rules it evaluated, whether it flagged anything for human review, and what final action it took. You should be able to recreate the agent's entire decision path.

Good observability also includes timestamps, so you know exactly when each step happened and how long it took.

How is observability different from just keeping logs?

Logs are raw data. Observability is structured insights. Imagine you're tracking a customer order.

A log file might show 200 lines of timestamps and system events. Observability shows you a clear timeline: order received at 9:03 AM, inventory checked and confirmed at 9:03 AM, payment processed at 9:05 AM, shipping label created at 9:10 AM, and the order shipped at 2:30 PM.

Both contain the same information, but observability structures it so humans can actually understand what happened without digging through pages of technical data.

What happens if AI agents operate without observability?

You lose control and trust. If your agent rejects invoices or flags transactions, but you can't see why, your team starts second-guessing its decisions. You can't improve the agent because you don't know where it's making mistakes versus correct judgments.

When auditors ask how a payment decision was made, you can't provide documentation. It's like having an employee who never explains their work. They might be doing everything correctly, but you can't verify it, can't trust it, and can't defend it to stakeholders.

Does observability slow down AI agents?

Done right, no. Recording what an agent does adds minimal overhead, similar to how security cameras don't slow down people working in a warehouse.

The key is capturing the right level of detail. You don't need to record every intermediate calculation, just the meaningful decision points.

For example, when an invoice gets processed, you record the extracted data, the matching result, and the approval decision, not the thousands of token-level operations the AI model performed. Modern systems handle this recording alongside the main work with negligible performance impact.

What are the risks if observability is incomplete or missing?

You can't troubleshoot problems effectively. When something goes wrong, your team wastes hours trying to figure out what happened instead of fixing it quickly. You can't prove compliance or pass audits because you can't show a clear decision trail.

Trust erodes because stakeholders can't verify that agents are working correctly. Your team might abandon AI automation altogether and go back to manual work or rigid scripts because they don't feel confident in what the agents are doing.

Zamp addresses this by building observability into the core of every agent. Activity logs record every action with full context. You can click on any transaction and see exactly what data the agent reviewed, which rules it applied, and why it made its decision.

When an agent flags something for "Needs Attention" status, you see the specific reason and the information that caused the flag. The dashboard gives you a real-time view of what's happening across all your agents, so you're never operating blind.

How detailed should observability data be?

Detailed enough that anyone on your team can understand what happened, not just technical people. If your agent extracts data from an invoice, observability should show the original invoice, the extracted fields, and whether any values were uncertain.

If it matches a line item to a purchase order, show both documents side by side with the matched fields highlighted.

The goal is that your AP manager can review an agent's work just like they would review a junior employee's work, without needing a developer to interpret logs or explain system behavior.

Can observability help improve AI agents over time?

Absolutely. Observability data shows you patterns in how agents perform. You might notice that your agent always flags invoices from a specific vendor because their invoice format is unusual.

Now you know how to update the agent's instructions for that vendor. Or you see that 80% of "Needs Attention" cases happen because suppliers put purchase order numbers in different fields.

You can train your agent to check multiple fields. Without observability, you'd just see a high exception rate and not know how to reduce it. Observability turns vague problems into specific, fixable issues.