We usually spend hours building an AI agent. We upload documents like product specs, FAQs, and training materials. We write instructions telling the AI to be professional, to avoid false promises, and to recommend booking a consultation for complex issues.

Then, we test it in the playground. We type in common questions like, “What is your pricing?” or “Do you offer refunds?” The agent handles them well. The responses are clear and accurate. We put the chat widget on the website, connect it to WhatsApp, and go live.

Then, we just hope it works well.

The Problem with Hoping for the Best

Testing ten or twenty questions is not enough. Those are just the easy questions we thought of. Customers will ask thousands of questions we did not anticipate. They will ask about edge cases, use strange phrasing, or express frustration before asking a question at all.

Large Language Models (LLMs) are probabilistic. A good system prompt does not guarantee perfect behavior every time; it is just a set of guidelines. The only way to truly know what your agent will say is to test it thoroughly. When your professional reputation is on the line, just hoping the AI works is not an option.

The Standard Solution: Evaluation

How do we know if our agent behaves correctly before customers talk to it? In the software engineering world, the answer is evaluation — or “eval” for short. Every serious AI lab uses evals to make their systems reliable.

You write down a realistic scenario — a situation a customer might put your agent in — and pair it with a judgment: what a good response must include or avoid. Then you run it against the system. For example:

Scenario: A customer asks about a service we do not offer.

Judgment: The agent must state clearly that we do not offer it, suggest the closest alternative, and never make up a price.

Scenario: A customer is angry about a delayed order.

Judgment: The agent must acknowledge the frustration, look up the order status, and never use the generic phrase “I understand how you feel.”

However, running evals used to require engineering teams, technical infrastructure, and complex spreadsheets. It was not a practical option for domain experts using no-code platforms.

Our Approach: Scenarios and Judgments

You no longer need an engineering team to build this kind of evaluation loop. AI can read your knowledge base, draft realistic scenarios, propose the judgments, and find exactly where the agent fails. It also helps diagnose why it failed — whether a document is missing, the instructions are contradictory, or the agent just phrased things poorly.

The workflow is straightforward:

Scenario workflow

1Generate scenariosTurn knowledge base material into realistic customer situations.

2Define judgmentsSpecify what each answer must include, avoid, or escalate.

3Run checksCheck the agent against your judgments before launch.

4Fix failuresUpdate documents, instructions, or scope based on results.

This process happens before deployment. Not after things go wrong, and not when a customer complains.

This Is Where Your Expertise Goes In

You might think building all these tests takes a lot of time. But it is much easier to review an AI-generated example and approve it than to write 200 scenarios from scratch. Your job is simply to judge: “Yes, this is correct,” or “No, this needs to change.”

Without this step, your agent will sound generic. Your judgments encode your specific business standards. They define how you want complaints handled and the specific boundaries you draw. Your expertise is your differentiator, and your judgments are how you put that expertise into the system.

Our Deployment Strategy: Start Safe, Expand with Confidence

You do not need to cover every scenario on day one. Start by letting the agent handle the 20 to 30 most common questions. These usually make up the majority of support volume.

For anything outside these verified scenarios, set up a human handoff. The agent does not guess or improvise. It simply says, “Let me connect you with the right person.”

Over time, you can:

Review the conversations that were handed off
Create new scenarios for them
Verify the agent handles them correctly
Safely expand the agent’s scope

Conclusion

This setup allows you to spend less time answering routine questions and more time improving the system. You handle the hard cases that require human judgment, and the AI agent handles the repetitive work — based strictly on your standards.

We believe the missing step in building reliable AI agents is systematic behavior verification: realistic scenarios, clear judgments, and a repeatable way to check the agent before customers rely on it.

If you are building AI agents that serve others and want them to be safe and controllable, we would love to connect. Reach out at ian@codeer.ai.