When the same workaround keeps interrupting your work, it may be worth building a small tool to handle it. Bryant Hood’s four-step framework—notice and explore, plan and premortem, execute and test, then deploy and maintain—offers a practical way to investigate the problem before asking an AI agent to help write software. It is an author’s account, not proof that agents reliably produce working tools or that the process will save a particular amount of time.
Start with the recurring friction, not the tool
Hood’s example began with a small, repeatable failure: he would finish a Microsoft Teams call and forget to retrieve its AI summary. Rather than starting with a feature list, he considered the underlying workflow and the information he wanted to keep.
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He describes a Windows program that watches for a Teams call, records both sides, transcribes the audio locally, and writes a transcript alongside Outlook meeting details. The audio is deleted after the transcript is written; a tray icon and a folder of text files provide the interface. These are details of Hood’s personal tool, not a general recipe for recording calls or handling their data.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Step 1: Notice and explore
Look for friction that recurs often enough to have become routine: copying information between apps, remembering a follow-up, or repeating a sequence of clicks. Before choosing an implementation, use a question-and-answer exchange with the AI agent to define the actual need.
- Describe what happens now, including the workaround and where it breaks down.
- Ask the agent to state the assumptions it is making about your computer, calendar, language, and work habits.
- Correct those assumptions and clarify constraints the agent has not asked about.
- Challenge its proposed solution; ask it to argue against its own recommendation and identify simpler alternatives or reasons not to build anything.
The purpose is to expose missing requirements while changing direction is still cheap. If the problem is not frequent or consequential enough to justify a tool, that is a useful outcome too.
Step 2: Plan and premortem
Write down the intended work and unresolved decisions before code exists. Ask the agent to account for the constraints you identified, distinguish settled choices from uncertainties, and describe how the tool should behave in relevant cases.
Then conduct a premortem: imagine the tool has failed or caused a problem, and ask what assumptions or design choices might have led there. Hood’s example illustrates why this matters. He wanted AI-generated call summaries, but recognized that using a remote AI service would send transcript text off the device. He removed the summary feature; the repost of his account describes it as disabled and the network client removed. That was his choice for this tool, not a claim that local transcription alone guarantees privacy or security.
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A written plan also gives you something concrete to test against. If a newly surfaced constraint changes the design, revise the plan before continuing. Hood’s account supports that kind of adjustment through the removed summary feature; it does not establish a broader iterative method beyond that example.
Step 3: Execute and test
Once the plan is clear enough, let the agent carry it out. Then run the result yourself in the environment where you expect to use it. As Hood puts it, “Reading it won’t tell you what running it will.” Reviewing code can reveal some issues, but it cannot establish whether the program behaves correctly in actual use.
- Try the normal workflow from beginning to end, not just the individual feature that is easiest to demonstrate.
- Check the constraints in your plan: for example, whether the expected files are created and whether data goes where you intended.
- Try foreseeable failure cases, such as an unavailable app or missing meeting information, if they matter to your use.
- When behavior differs from the plan, describe the result to the agent, revise the implementation, and test the affected workflow again.
These checks are practical applications of Hood’s instruction to run the tool and see whether it works in the real environment; the source does not report a formal test protocol or reliability results.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 4: Deploy and maintain
A successful run on the development machine is not the same as a usable installation elsewhere. Hood’s final step is to deploy the tool—install or hand it to a machine that has not seen it before—and keep maintaining it afterward.
Use that handoff to discover dependencies or setup steps your development environment supplied invisibly. Confirm that the intended user can start the program, reach its files, and use the core workflow. After deployment, treat updates and repairs as part of owning the tool, rather than assuming an agent-built result will remain useful without attention.
What the framework can—and cannot—tell you
Hood says the same process also produced a personal lint script, a wiki maintained by an agent, and a task queue. Those are reported examples, not independently examined products or measured case studies. The account gives no quantified time savings, reliability comparison, or evidence that this approach suits every workflow.
Use the four steps as a way to structure a small experiment: investigate the real need, make assumptions and data decisions explicit, test actual behavior, and plan for deployment and upkeep. Whether building is worthwhile depends on the specific workflow and the effort it takes to implement and maintain a solution.
Sources: Bryant Hood’s article and the repost of his account.
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