Start with the problem
An AI agent is most valuable when its purpose is clear. Instead of asking what the latest model can do, start by identifying a repeated task, the people who perform it, and the outcome they need.
Give the agent a specific job, measurable boundaries, and a human who remains responsible for important decisions.
A real-world use case
Consider a university information assistant. Students ask about course requirements, registration steps, and available learning resources. The assistant can search approved documents, explain the relevant guidance, and point students to the original source.
The goal is not to replace university staff. It is to make routine information easier to find and route uncertain questions to the right person.
Designing the workflow
- Define the questions the assistant should answer.
- Collect approved, current source documents.
- Retrieve relevant passages before generating a response.
- Include source references and acknowledge missing information.
- Escalate requests that need a person's judgment.
Security and reliability
Treat uploaded documents and model responses as untrusted input. Keep private records behind access checks, give tools the minimum permissions they need, and require confirmation before consequential actions.
Test ordinary questions alongside ambiguous requests, outdated documents, and attempts to bypass the rules. A useful assistant should know when not to answer.
Measure usefulness
Evaluate answer accuracy, source quality, successful task completion, and the effort required from the user. Review failures with the people who actually use the system rather than relying only on a polished demonstration.
Build small, then improve
Start with a narrow, read-only assistant. Expand its capabilities only when the existing workflow is reliable. Good agents are built through careful iteration, not through giving a model unrestricted autonomy.
