Use the minutes while an AI agent runs to do the work it can't: think, verify, and understand the system. That short answer is the whole article. The rest is how.
A new kind of pause has entered the developer's day. You describe a task, hand it to a coding agent, and watch it read files, run tests, and write code. Sometimes it finishes in two minutes. Sometimes it takes an hour.
Many developers fill that gap by scrolling their phones or opening another tab. Others sit and stare at the terminal. Both waste the most valuable thing a programmer has now: attention the machine doesn't need.
The job has moved from typing to deciding.
The agent now handles much of the how. You still own the what and the why. An analysis of roughly 400,000 Claude Code sessions found that people made most of the planning decisions, while the AI made more of the implementation decisions (Level Up Coding).
Trust is still the bottleneck. In one developer survey, 46% said they distrust AI accuracy to some degree, and only 3% trust it highly (Superdev Academy). Generating code is cheap. Knowing it is correct is not.
That gap is where your idle minutes belong.
Seven things to do while the agent works
Write the tests first, or in parallel. Tests are your contract with the agent. If you define what "done" means while it builds, you can judge its output in seconds instead of guessing.
Review the plan, not just the diff. Most agents show a plan before they act. Read it line by line. A wrong assumption caught at the plan stage costs one sentence to fix; caught in a 600-line diff, it costs an afternoon.
Reproduce the problem by hand. Run the bug yourself, trace the request through the system, and read the logs. You'll know whether the agent fixed the cause or only the symptom.
Read the code around the change. The agent sees the files it opens. You should see the whole system: data flow, business rules, deployment, and why past decisions were made. That context is what lets you give better instructions next time.
Prepare the next task. Break the next feature into clear, small prompts with acceptance criteria. Well-scoped tasks are the single biggest lever on agent output quality.
Think about security and edge cases. AI code often looks right and fails at the edges: empty inputs, auth checks, rate limits, and injection. List what could break and check for it when the agent returns.
Talk to users and the business. The highest-value work is deciding what to build. Use the time to answer a support ticket, review analytics, or sketch the next product idea.
What not to do
Don't accept code you can't explain. If you couldn't walk a teammate through it, you don't own it yet. Ask the agent to explain its assumptions, then check them against the code and tests.
Don't stop coding entirely. Rebuild a small piece of generated code yourself now and then. Skills you never use fade, and you'll need them when the agent gets stuck.
Don't run five agents you can't supervise. Parallel agents multiply output and review work alike. Run only as many as you can actually check.
Don't treat waiting as a break. A real break is fine. Half-watching a terminal while half-reading social media is neither rest nor work.
The bottom line
AI hasn't made programmers idle. It has changed what their time is worth. Typing was never the hard part of software; understanding the problem, judging trade-offs, and verifying the result always were.
The developers who thrive will treat every agent run as a window for that harder work. Draw the logic, set the standard, and check the output. Let the machine type.
