Less AI theater. More useful automation.
How to choose a first AI agent project that solves a real operational problem.
Choose a bounded workflow
The strongest first AI project often starts with an ordinary operational bottleneck: triaging incoming requests, finding information in internal documents, or preparing a draft for review. Define the inputs, the desired output, and the person responsible for the final decision. A narrow workflow is easier to evaluate and improve than an assistant that claims to do everything.
Understand the process before adding a model
Map the existing steps and identify where deterministic software is enough. Rules, database queries, and ordinary integrations remain useful. Use a model where language understanding or flexible interpretation adds value. Keep the model’s tools scoped to the minimum access needed, and require review before sensitive or irreversible actions.
Evaluate with real examples
Create an evaluation set from representative, permissioned examples. Include incomplete requests, ambiguous language, and cases where the right response is to ask for clarification. Compare outputs against a documented standard. Measure task completion, errors, review time, and operational cost instead of judging the project only by how persuasive the demo looks.
Design the handoff
People need to understand what happened and what needs attention. Show supporting evidence, preserve an audit trail, and make it easy to correct or reject an output. Define timeouts, fallbacks, and escalation paths. A useful agent fits into an existing team’s work and makes the next step clearer.