Simulation and evaluation are pre-deployment testing methods used to check whether an AI agent reaches the intended outcome and follows policy before it is allowed to operate on real users, data, or systems. Simulation recreates realistic tasks, conversations, tool calls, and error cases in a safe environment. Evaluation measures the agent’s behavior against success criteria such as correctness, refusal behavior, escalation rules, and permission boundaries.
In cyber security, this matters because agents can be exposed to prompt injection, malicious user inputs, and workflow abuse once they connect to business tools. Good evaluation helps defenders find overbroad access, unsafe action patterns, poor escalation logic, and policy violations before deployment. It is also useful for red-team style testing, where teams try to make the agent leak data, bypass approvals, or perform unauthorized actions. Strong simulation and evaluation make agent systems more predictable, auditable, and safer to place in production.