Service
AI Agents
One job, one agent, one number that says whether it works.
The problem
An agent that can do anything is an agent nobody can evaluate. Broad scope is why agent pilots stall: there is no threshold at which someone can say it succeeded, so it never graduates from demo to production.
One job, one agent, one number
An agent receives a single objective, reasons about it, and calls the tools it needs. The step that decides whether it can be deployed is not the reasoning but the confidence check: what the agent does when it is not sure, which is to hand back to a human rather than guess.
What we actually do
- Narrow the scope until success is a number you can put on a dashboard.
- Define what the agent does when it is not confident, before defining what it does when it is.
- Keep a human in the loop wherever the cost of being wrong exceeds the cost of asking.
- Log every decision with its inputs, so a bad outcome can be explained rather than guessed at.
- Run it against historical cases before it touches a live one.
When this is the wrong answer. The task has a fixed set of rules and no judgement in it. Then it is automation, it is cheaper, it is deterministic, and it will still be running in five years. See AI Automation.
Problems this solves