How to Test AI Agents Before They Touch Real Systems

Every tool is deterministic code and should have ordinary unit and integration tests: valid inputs, invalid inputs, permission boundaries, timeout and retry behavior. This layer catches the bugs that matter most — the agent can only be as safe as its least safe tool.

Pay special attention to idempotency: what happens if the agent calls create_invoice twice? Tools should make duplicate calls safe or detectable, because agent retries will eventually produce them.

Record real workflow instances (sanitized) as replayable scenarios: input, expected decision, expected tool calls, expected output. Run the whole agent against this library in a sandbox with mock or staging systems after every change to prompts, models, or tools.

The library is a living asset: every production surprise becomes a new scenario. Teams that do this can change models confidently; teams that do not are locked to whatever worked by luck first.

Deliberately attack the agent: prompt injection through user content and retrieved documents, requests outside policy, malformed inputs, tools returning garbage, and the classic — a user asking the agent to exceed its authority "just this once".

Score containment, not perfection: when the agent is fooled, what is the worst it can do? If the answer is "send an email that needs editing", you are safe; if it is "move money", redesign before launch.

In shadow mode the agent receives real inputs and produces real decisions that humans still execute. Measure agreement rate per action type. Two to four weeks of shadow data tells you more than any amount of sandbox testing.

Then promote action types one at a time: read-only actions first, then reversible writes with approval, then — where the numbers justify it — autonomous execution with spot checks and instant rollback. Keep the approval UI; you will return to it for every new workflow.