A Real Agent in Action

The best way to understand how an agent works is to watch one work. In this lesson we will follow a research agent step by step through a realistic multi-part task. You will see exactly how the perceive-reason-act loop plays out in practice, how the agent chooses and uses tools, how it handles a problem mid-task, and how it knows when the job is done. Everything described here reflects how real agent systems operate.

The Mission

The user gives the agent this instruction: Research the three most widely used renewable energy sources worldwide in 2024, write a two-paragraph summary for each, and save the complete report as a text file called renewable-report.txt. This task requires: finding live information, synthesizing it into structured writing, and saving a file. A basic chatbot can write about renewable energy from its training data — but it cannot look up 2024 figures, cannot verify live rankings, and cannot save any file. An agent with web search and file-write tools can do all of it.

The Walkthrough: Step by Step

Step 1 — Plan. The agent reads the goal and outlines what it will need to do: search for 2024 renewable energy rankings, identify the top three sources, search for details on each, write two paragraphs per source, and save the output. It notes which tools it will use: web search and file write. Step 2 — Search for rankings. The agent calls the web search tool with the query: top three renewable energy sources globally 2024 usage statistics. The tool returns excerpts from several reports. The agent reads them and identifies solar, wind, and hydropower as the top three based on installed capacity data. Step 3 — Deepen research on source one. The agent calls web search again: solar energy global capacity 2024. The result returns current gigawatt figures, leading countries, and growth trends. The agent stores this in its working context. Step 4 — Deepen research on sources two and three. The agent repeats the search for wind energy and hydropower, accumulating specific 2024 data for each. Step 5 — Handle a problem. The wind energy search returns conflicting figures from two different organizations. The agent notes the discrepancy, searches for a third source to break the tie, and uses the figure that two out of three sources agree on. It flags the discrepancy in its draft. Step 6 — Write the report. With all three data sets in its context, the agent writes two paragraphs per energy source — accurate, specific, and based on the data it retrieved. Step 7 — Save the file. The agent calls the file write tool with the filename renewable-report.txt and the full report text as the content. The tool confirms the file was saved. Step 8 — Verify completion. The agent checks its goal: ranked top three sources, yes; two paragraphs each, yes; saved as renewable-report.txt, yes. Goal achieved. The agent reports back to the user with a summary of what it did.

What Made This Work

Several things had to go right for this agent to succeed. The goal was specific enough that the agent could recognize when it was done. The tools were the right ones for the task — web search for live data, file write for the output. The agent's reasoning was strong enough to handle the conflicting data in step 5 rather than crashing or picking the wrong figure blindly. And the agent tracked its working context across all eight steps, never losing track of the goal. If any one of those elements had failed — a vague goal, the wrong tools, weak reasoning, or lost context — the task would have produced a wrong or incomplete result. Good agent outcomes depend on all the pieces fitting together.