Summary
A useful AI agent starts with a well-defined workflow. Give it the context and tools it needs, set clear boundaries, and evaluate its results before expanding its responsibilities.
1. Choose a workflow with a clear outcome
Start with a repeatable task such as sorting support requests, preparing a research brief, or extracting details from documents. Define the input, the expected output, and what a successful result looks like. A narrow first use case makes it easier to measure value and spot errors.
Map the steps a person follows today, including where they look for information and when they ask for help. That map becomes the first version of the agent workflow.
2. Give the agent the right context and tools
Provide access only to the sources and actions the task requires. A support assistant might search a knowledge base and prepare a draft, while an operations assistant might look up a record before proposing an update. Keep instructions explicit about what to do when information is missing or conflicting.
Separate reading information from taking actions. For consequential changes, show the proposed action and its evidence to a person before it is carried out.
3. Evaluate before expanding
Collect representative examples, including ambiguous requests and failures. Check accuracy, completion rate, time saved, and whether the agent asks for help at the right moments. Review traces and user feedback to improve instructions and tool responses.
Start with a small rollout, monitor outcomes, and expand the workflow only when the results are dependable.