How to Build Launch Confidence in an AI Agent Before You Have Enough Data for a Golden Eval Set

Quick answer

Before you have real conversation data, build your eval set from three substitute sources instead of waiting: synthetic queries generated from known support tickets or sales call transcripts, adversarial and edge-case prompts written by the team that knows where the product is weird, and a small human-run pilot with a controlled group of real users whose transcripts become your first genuine eval data. None of these fully replaces a golden set built from months of live traffic, but together they get you to a defensible launch decision.

The chicken-and-egg problem is real, but not unsolvable

A golden evaluation dataset is normally built by curating real, representative conversations and their correct outcomes. A pre-launch agent has no real conversations yet, which makes the standard approach circular: you need traffic to build the eval set, but you need the eval set to justify sending traffic to the agent. The way out is not to skip evaluation, it is to accept a weaker, substitute eval set for launch and treat the first weeks of real usage as an active data-collection phase that upgrades it.

Source one: synthetic queries from adjacent real data

Even without agent conversations, you almost certainly have adjacent real data: past support tickets, sales call transcripts, help center search queries, or FAQ traffic. Convert a representative sample of these into the phrasing a customer would actually use with the agent, and use them as your first eval prompts. This is weaker than real conversation data because it was not generated against the actual agent, but it is far stronger than eval prompts invented by the product team, which tend to be too clean and too aligned with how the team thinks about the problem rather than how customers actually phrase it.

Source two: deliberately adversarial and edge-case prompts

Have the people who know the product's actual weak points, not a generic red-team checklist, write a batch of prompts designed to break the agent: ambiguous requests, multi-intent messages, requests just outside the agent's scope. This overlaps with pre-launch red-teaming for security purposes, but the eval-set version is broader: you are not just checking for jailbreaks and prompt injection, you are checking for ordinary failure modes a real, slightly confused customer will hit in week one.

Source three: a controlled pilot before the full launch

Run a limited pilot with a small, consenting group of real users, internal employees, design partners, or a small opt-in segment, before the full rollout. This is the fastest way to get genuine conversation data, and every transcript from it becomes eval-set material for the next iteration. Keep the pilot small enough that a bad answer has limited blast radius, but real enough that the conversations are representative of what the full launch will look like.

Weight your confidence accordingly

Be explicit internally about the fact that a cold-start eval set is a lower-confidence signal than a mature golden set, and factor that into the launch readiness decision rather than presenting pass rates on synthetic data with the same confidence you would give a golden set built from six months of real traffic. A reasonable pattern is to launch with tighter guardrails and a lower autonomy ceiling than you eventually intend, and loosen both only after the pilot and early live traffic have converted the substitute eval set into a real one.

FAQ

Q: How many synthetic and adversarial prompts do we need before we can call it an eval set?

There is no universal number, but a few hundred prompts spanning your main use cases plus a meaningful adversarial slice is a reasonable floor for a pre-launch check. What matters more than the count is coverage: make sure every major intent category and every known edge case has multiple prompts, not just the happy path.

Q: Should we delay launch until we have a real golden eval set?

Usually not, if the alternative is delaying indefinitely since you cannot get real data without launching something. The better move is a narrower, more supervised launch that generates real data quickly, rather than an indefinite delay chasing a mature eval set you cannot build without users.

Q: How fast should the substitute eval set get replaced by real data?

As fast as your pilot and early launch traffic allow. Set an explicit checkpoint, for example after the first few thousand real conversations, to rebuild the eval set from live data and re-run your quality bar against it rather than continuing to rely on the synthetic version indefinitely.