AI & Automation GreaterTechHub Editorial
Building AI agents around clear, reviewable workflows
A practical look at choosing bounded tasks for AI agents, connecting the right information, and keeping people involved where judgement matters.
An AI agent is most useful when it has a defined job, suitable information, and a clear boundary for when to ask a person for help. Treating an agent as a workflow component—not an unbounded replacement for a team—makes it easier to evaluate and improve.
Start with a task, not a model
Map the steps people already follow, the systems they consult, and the decisions they make. Then choose a repeatable task with an observable outcome, such as routing an enquiry or preparing a first draft for review.
A narrow starting point makes it possible to compare the assisted process with the existing one and decide whether the change is genuinely useful.
Make access and hand-offs explicit
Give an agent access only to information needed for its task. Define what it may prepare or update, what needs approval, and which situations must be escalated to a person.
Keep useful records of the input, relevant source material, and action taken. This helps teams investigate unexpected results and refine the workflow responsibly.
Evaluate with real scenarios
Test ordinary requests as well as incomplete, ambiguous, and out-of-scope cases. Review output quality, hand-off behavior, and the amount of correction work required before expanding access or responsibility.
For teams exploring implementation, our AI agents and chatbots and AI automation services can help assess a practical use case.