AI / Agent Validation

For decades, testing has answered one question: does the system behave as specified. AI agents move the question. An agent interprets context, chooses between options, calls tools, and decides what to do next. Every call can succeed and the decision can still be wrong.

That is a different kind of defect. An agent in procurement can create a purchase order flawlessly and still pick the wrong supplier, ignore an existing contract, or step past an approval threshold. The process ran. The judgment behind it did not hold.

When you need this

• An agent is being piloted inside a real process — procurement, service, finance — and nobody has defined what correct means for it.
• A vendor has offered an agentic feature on top of a system you already run, and you have to decide whether to switch it on.
• Agents are already acting, and your risk or audit function has started asking how their decisions are verified.

What we do

• Define acceptable behavior. A regression test expects one input to produce one output. An agent may not be deterministic. We work with your process owners to define a range of acceptable decisions, and the line the agent must not cross.
• Validate the decision, not only the output. We test whether the agent respected policy, thresholds, existing contracts and approval rules, not only whether it produced something.
• Test under uncertainty. Incomplete data, contradictory context, a system that returns something unexpected. We check what the agent does when the situation is not clean.
• Check consistency over time. The same agent, the same case, a slightly different context. Drift is the failure mode that never shows up in a single test run.
• Put guardrails and oversight in place. What gets logged, what escalates to a person, and what stops the agent. Validation without an off switch is an opinion.
• Monitor after go-live. Agent behavior is not fixed at release, so validation here is continuous rather than a phase before it.

Where agents are built on UiPath, we validate them with the platform's own agentic and testing capabilities, so the evidence lives where the agent does.

Traditional testing does not go away. Functional, integration, security and performance testing all still apply. Agent validation sits alongside them and covers the part of the risk they were never built for: an action that was executed perfectly on the basis of poor judgment.