An AI agent is software that can interpret a task, decide on steps and use tools, such as searching documents or updating a record, to complete it. In a demo, that feels transformative. In production, the questions change: which systems can it touch, who approves its actions, and how do we know it is right?
01Start with a process, not a model
The best first use cases have a clear owner, a high volume of similar work and an easy way to check the result. Supplier invoice intake, tenant service request triage and contract clause review are typical examples. Each has inputs you can sample and outputs your team can verify.
02Connect to systems through governed tools
Agents become useful when they can read and write to business systems. They become safe when that access is narrow and explicit: a defined set of tools, each with permissions that match the role the agent is playing, and an approval step for anything with financial, legal or customer impact.
03Measure before you scale
- Build an evaluation set from real historical examples.
- Agree accuracy and turnaround targets with the process owner.
- Log every agent action and every human override.
- Review results regularly and treat prompts, tools and models as maintained assets.
The goal is not an AI that replaces judgement. It is an AI that removes the work standing between people and the decisions only they should make.
Organisations that follow this path move from one successful agent to a portfolio of them, each delivering measurable value and each governed the same way.