An AI assistant can answer a question or draft a response. An autonomous AI agent can take a goal, choose its next steps, use connected tools, and check the results as it works.
Imagine an agent tasked with preparing a weekly report. It might retrieve information, identify missing data, draft a summary, and flag an unusual result for a person to review. The agent handles the steps between the request and the draft report.
Give the Agent a Small, Clear Job
A useful first project is a task your team already understands well. Define the inputs, the expected output, and the conditions that should cause the agent to stop or ask for help.
Then test it against real examples. Can you see which tools it used? Can you check the information behind its conclusions? Does it handle an unexpected result sensibly? Those answers matter more than a polished demo.
Decide What Requires Approval
An agent that reads information has a different level of responsibility from one that updates a database or sends a message. Give it access only to what its task requires, record its actions, and put a person in charge of consequential decisions.
Autonomous agents become more useful when their scope is clear and their work is easy to inspect. I explore how they work and where businesses might use them in my latest article:
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