Give an AI agent one clear job and clear limits.
An AI agent can work with varied information, find relevant documents and prepare an action using approved tools. It needs a defined purpose, permitted data sources, quality checks and someone accountable for the outcome.
When an agent makes sense
For example, when incoming documents vary in format, a task requires context from several sources or the next step needs a drafted response or proposal. If simple rules can reliably do the work, we use ordinary automation instead.
What we define before development
The task, expected output, approved inputs, tools, permissions, acceptable errors, review points and the process owner. The agent must know when to stop and send an uncertain case to a person.
How we validate it
We test normal, unusual and difficult examples. We check accuracy, time spent reviewing results, model costs and behavior when information is missing. We introduce a working system in stages.
One agent or several?
We use multiple connected agents only when a process genuinely needs different functions. The goal is reliable work, not a large number of agents.
Where is manual work slowing your business down?
Tell us about one process, where it gets delayed and which systems are involved. In a 30-minute call, we will discuss whether a workshop, process review or another first step makes sense.
The initial call is an introduction, not a technical audit. Any further work is scoped and agreed separately.