A risk committee asks a simple question: “How do you know the agent is doing its job?” Most teams answer with a demo. A demo shows one good run. It says nothing about the thousandth.
Treat an agent like a new employee on probation. You would not judge them by their best day. You would look at the work they finish, the mistakes they make and who catches those mistakes.
Track four numbers
Task success rate. Out of all tasks the agent started, how many ended correctly? Define “correctly” before launch, with a person who knows the work. Check a sample every week by hand.
Escalation rate. How often does the agent stop and ask a human? Too low can mean it is guessing. Too high means it is not saving anyone’s time. Watch the trend more than the value.
Cost per completed task. Add model calls, tool calls and retries. An agent that loops quietly can double your bill without a single visible error.
Time to finish. Measure from request to done, including the wait for human approval. This is the number your users feel.
Keep a trace of every run
For each task, store the request, every step the agent took, every tool it called, what came back, and the final result. When something goes wrong, this trace is the only way to learn why. Without it you are guessing, and so is the vendor.
Build a small test set before launch
Collect 50 to 100 real cases, including the awkward ones: missing fields, angry customers, documents in the wrong format. Write down the right outcome for each. Run the set after every change to a prompt, a model or a tool. If a change makes the score drop, you find out in minutes, not from a customer.
Signs of trouble
- Success rate slides slowly over weeks. The world changed and the agent did not.
- The same tool is called many times in one task. That is usually a loop.
- Humans approve every request without reading. The approval step has become decoration.
- Nobody can say who owns the agent after launch.
Name an owner
An agent without an owner decays. Pick one person who reads the weekly numbers, reviews failed cases and can pause the agent. Give them a clear stop button.
If you want help setting this up, see our AI agent development and LLMOps work, or start from the readiness checklist.
Work with us on this
- AI Agent Development: We build AI agents that complete multi-step work inside your systems, with guardrails, human approval and full audit logs.
- MLOps & LLMOps: Evaluation, monitoring, cost tracking and release processes that keep AI systems reliable after launch.