Testing AI Agents: Evals Before They Touch Customers

Building an AI agent demo takes an afternoon. Building one you can trust with customers, money or real business records takes discipline, and most of that discipline is testing. An agent that works on the five examples you tried can still fail on the sixth, and unlike traditional software, it can fail confidently, with a well-written answer that is simply wrong.

The fix is evaluation, usually shortened to "evals": a repeatable way to measure whether an agent does the right thing, run every time you change a prompt, a model or a tool. This guide covers how to build evals that catch real problems before your customers do.

Why AI Agents Need a Different Kind of Testing

Traditional software is deterministic: the same input gives the same output, so a unit test either passes or fails. AI agents are different:

• Outputs vary. The same question can produce different wording, different tool calls or a different order of steps.
• Correctness is often a judgment. Was the summary accurate? Was the tone right? Did the reply answer the actual question?
• Small changes ripple. Editing one sentence in a prompt, or upgrading the model, can fix one behavior and break three others.
• Agents take actions. A chatbot that says something wrong is embarrassing. An agent that issues the wrong refund or emails the wrong customer is a real incident.

So instead of a handful of pass/fail unit tests, agents need a test suite of realistic scenarios, scored in several ways, tracked over time.

Step 1: Build an Evaluation Dataset

The foundation is a set of realistic test cases, each with an input and a description of what "good" looks like. Good sources:

• Real requests. Past emails, support tickets or chat logs (with personal data removed) are the best source, because they reflect how people actually ask.
• Known failures. Every time the agent gets something wrong in testing or production, add that case. Your dataset becomes a record of every lesson learned.
• Edge cases. Ambiguous requests, missing information, angry customers, requests the agent should refuse, and attempts to trick it.

Start with 30 to 50 cases that cover your most important workflows. Grow it steadily. A few hundred well-chosen cases is enough for most business agents.

Step 2: Score in Layers

Deterministic checks

Wherever you can, check facts with code. Did the agent call the right tool? With the right customer ID? Did it avoid calling the refund tool when it shouldn't have? Is the output valid JSON? These checks are fast, cheap and unambiguous, so use them first.

LLM graders

For judgment calls, such as whether a reply was accurate, complete and polite, a second model can grade the output against a written rubric. LLM graders scale well, but they need calibration. Compare their scores to human judgments on a sample and tighten the rubric until they agree.

Human review

For high-stakes workflows, keep humans in the loop on a sample of outputs. Their judgments become the ground truth that keeps automated graders honest.

Step 3: Test the Actions, Not Just the Words

For agents that use tools, the most important question is what did it do? Run evals against a sandbox or test environment with realistic data, and check the end state: was the right record created, with the right values, and nothing else touched? An agent can write a perfect confirmation message after doing the wrong thing.

Pay special attention to anything involving money, customer communication or deleting data. These actions should be tested hardest, and in production they should usually require human approval until the agent has earned trust.

Step 4: Run Evals on Every Change

Evals are only useful if they run consistently. Wire them into your workflow so every prompt edit, model upgrade or tool change runs the full suite and reports the scores next to the previous version. Treat a drop in scores like a failing build: investigate before shipping. Our guide to building AI agents covers where evals fit in the overall architecture.

Step 5: Shadow Mode, Then Gradual Rollout

Before an agent acts on its own, run it in shadow mode: it processes real requests and proposes what it would do, while humans keep doing the work. Compare its proposals to what the humans actually did. When agreement is high and the misses are minor, let it act on low-risk cases first, then expand.

Step 6: Keep Measuring in Production

Testing doesn't end at launch. Log every agent run: inputs, tool calls, outputs and outcomes. Watch for:

• Human corrections. Every time a person overrides the agent, that is a free, high-quality test case. Feed it back into the dataset.
• Drift. New kinds of requests the dataset doesn't cover yet.
• Silent failures. An agent run that "succeeded" but didn't produce the real-world result, such as an email that was drafted but never sent.

The strongest agent systems treat every correction as durable: it changes the dataset, the prompt or the rules, so the same mistake doesn't happen twice. For more on putting agents to work, see AI agents for business automation and our complete guide to AI and automation.

Frequently Asked Questions

What is an AI eval?

An eval is a repeatable test of an AI system's behavior: a set of realistic inputs, a definition of what good output looks like, and a scoring method. Running evals on every change shows whether the system is getting better or worse.

How many test cases does an AI agent need?

Start with 30 to 50 cases covering your core workflows and known edge cases, then add every real failure you find. Most business agents are well served by a few hundred carefully chosen cases.

Can one AI model grade another?

Yes. LLM graders are widely used for judgment-based scoring, but they should be calibrated against human reviews and paired with deterministic checks wherever facts can be verified with code.

When is an AI agent ready for production?

When it scores consistently well on a representative eval set, has performed well in shadow mode on real traffic, and has guardrails such as human approval for high-risk actions. Expand its autonomy gradually as it proves itself.

Related reading: Building AI Agents, Prompt Engineering for Business Applications, and Model Context Protocol (MCP).