How often should enterprises red team their AI agents?

Red team on triggers, not on a calendar. The triggers that should force a test:

• Model version change

• System prompt change

• New tool or MCP server connection

• New data source added to retrieval

• Permission scope expansion

• Any guardrail configuration change

Layer continuous automated suites running on every deployment underneath that, and a deep manual exercise once or twice a year.

Annual red teaming is a compliance artifact. The system you tested in March is not the system running in June, because the provider updated the underlying model, someone added a tool, and the system prompt was edited four times. Each of those changes can reopen an attack path that the March test confirmed closed.

The distinction that gets missed: cadence and coverage are separate problems. Testing quarterly with the same probe library tells you nothing new after the first run. Testing continuously with a static suite catches regression but never finds a novel path. You need automated suites for regression and human adversaries for discovery, and they answer different questions.

Automated coverage should run in CI with a tracked attack success rate, so a change that degrades resistance blocks the deploy the way a failing test would. Manual exercises should be objective-driven rather than technique-driven. Set a goal an attacker would actually want, such as extracting another tenant's data or triggering an unauthorized write, and let the team find any path to it.

Akto provides continuous automated red teaming against agentic and LLM applications, which covers the regression half so human exercises can be spent on discovery.

If your last red team predates your current model version, you have not tested this system.