Testing for Control: Piloting Agentic Workflows Safely

Agentic systems don’t fail in the lab — they fail in the wild. What works perfectly in a controlled test environment can unravel quickly when exposed to messy inputs, real users, and unintended edge cases. That’s why smart firms don’t pilot agents like they pilot other software.

The goal isn’t just to prove functionality. It’s to learn what happens when the system acts without direct supervision. That requires a different test plan — one built around behaviour, escalation, and boundary conditions.

In practice, this means defining clear objectives for the agent (“What are we trying to automate?”), monitoring its decisions in real-time (“When did it act? What did it skip? What did it escalate?”), and capturing not just what it got right, but how confidently and appropriately it made each choice. Guidance from AI guardrail frameworks shows how to structure these controls.

The key is to test in constrained environments where feedback is immediate and the stakes are low. Internal reporting, task assignment, follow-up generation — these aren’t flashy use cases, but they’re rich in learning signals. They let you watch the system behave, assess its instincts, and course-correct without downstream risk.

Crucially, these tests aren’t just technical — they’re cultural. Piloting autonomy also means piloting trust. Stakeholders need to see the system behaving responsibly, not just accurately. They need to feel it’s in control, even when it’s out of sight.

Agentic systems aren’t just code — they’re co-workers. And like any new hire, you don’t throw them into critical tasks on day one. You onboard them. Supervise them. And scale them once they’ve earned it.