Agentic Testing: Checking That AI Agents Do What the Business Expects

Agentic testing is the practice of checking whether AI agents behave reliably before and after they go into use. An AI agent is software that carries out a series of tasks on its own, for example answering a customer request, retrieving data and updating a record. Because such agents do not always produce the same result twice, testing must go beyond a single pass-or-fail check. It looks at whether the agent reaches the right outcome, stays within the rules it was given and handles unexpected situations safely.

For leaders, agentic testing turns a technical question into a question of business assurance. Test results show where an agent can be trusted, where a person must stay involved and which risks remain. This supports compliance and risk management, because decisions about AI can be explained and evidenced. Rules such as the EU AI Act and the EU Cyber Resilience Act (CRA) increase the need for such evidence.

Testing is typically repeated whenever an agent, its data or the underlying AI model changes. Published benchmarks such as AgentBench give organisations a shared yardstick to compare how well different agents perform tasks. Within data and AI governance, agentic testing provides the proof that agents keep working as intended.