Agents need evals, not demos

Every agent project starts with a demo, and most of them look great. The model answers confidently, the workflow completes, the room nods. Then the agent meets real customers, real data and real edge cases, and the gap between the demo and production becomes the entire project.

The fix is not a better demo. It is an evaluation suite that exists before the agent does: a set of representative cases, a definition of what a correct outcome looks like for each, and an automated way to run the agent against all of them every time anything changes — the prompt, the model, the tools, the data.

We treat evals the way good engineering teams treat tests. They are written first, they run on every change, and a regression blocks a release. The difference is that agent evals are rarely binary; they score behavior along several dimensions (correctness, safety, tone, cost, latency) and the team decides which trade-offs are acceptable.

Once an eval suite exists, the conversation with stakeholders changes. Instead of 'does it work?', the question becomes 'it resolves 91% of tier-one cases correctly, escalates 8% and gets 1% wrong — is that better than today?' That is a question a business can actually answer.