To get better code from an AI agent, give it a clear outcome, the project context and tools it needs, small steps it can complete and you can review, and checks that show whether the result works. “Write code” asks for an artifact; a useful workflow tells the agent what problem to solve and gives you a way to catch mistakes.
How should you use an AI coding agent?
Keep ownership of the goal and the standard for success while letting the agent handle suitable implementation work. Anthropic’s June 16, 2026 analysis of roughly 400,000 Claude Code sessions from October 2025 through April 2026 attributed about 70% of planning decisions to people and about 20% of execution decisions to them, on average. The report summarizes this division as: “People decide what to build, and the agent decides how to build it.” Those figures describe Anthropic’s analysis and decision-attribution method; they are not a universal split for every coding agent or project.
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A practical workflow makes the work legible and reviewable:
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Define the outcome. Describe the user-visible or technical result you want, the relevant constraints, and what evidence would count as done. Include important cases the change must handle.
- Provide context and access. Point the agent to the relevant files, project conventions, interfaces, logs, or other tools it needs. In its Codex case account, OpenAI describes environment design and exposing UI, logs, and metrics so an agent can investigate and validate work. That is company-reported practice, not a guarantee of results in other environments.
- Divide the work. Have the agent investigate or propose a design, implement a bounded change, review it, and test it. A small change is easier to inspect and easier to undo than a broad, opaque rewrite.
- Check the result. Inspect the changes, run relevant tests or the application, and look at failures or other signals. Choose checks that match the task; a successful run alone may not establish that the code handles important cases.
- Iterate from evidence. Give the agent a specific failure, unexpected behavior, or gap to address, then inspect the revised work. Do not treat a confident explanation as proof that the issue is fixed.
OpenAI’s account describes a depth-first process organized around design, code, review, and test blocks. Microsoft Research’s 2025 qualitative study of more than eight hours of curated video likewise observed cycles of prompting, evaluating code, testing the application, and manual editing. These accounts support a process built around context and feedback; they do not prove that one prompt format or workflow is best for every task.
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What should you tell AI before it starts coding?
Give the agent enough information to distinguish the intended result from plausible but wrong implementations. A useful task description states the outcome, boundaries, and evidence you will use to judge completion. For example, rather than asking for a settings page, specify which settings it should expose, how changes should be saved, what existing behavior must remain unchanged, and how you will verify it.
Also make the project environment usable: identify relevant files or interfaces, point out conventions the agent should follow, and provide access to appropriate tools or test signals when available. OpenAI’s Codex case account emphasizes structure and tools, smaller work blocks, and visibility into UI, logs, and metrics. It is a report of one company’s practice, not evidence that providing any particular context will make an agent correct.
More detail is not automatically better. A long specification can still omit a critical constraint, and these sources do not establish a universally best prompt template. Prefer relevant context and clear acceptance checks over a large pile of instructions.
How do you check code written by AI?
Treat generated code as a proposed change, not a verified result. Review whether the changes match the request, run checks appropriate to the project, and examine what happens in the application or other relevant environment. If a check fails, use the actual error or behavior to guide the next iteration.
Simply running generated code is a limited check: it may show that one path executes, but it does not establish that the behavior is correct, that edge cases work, or that the change fits the rest of the project. A September 2026 arXiv preprint by Gabrielle O’Brien, Reed Milewicz, and Nasir Eisty analyzed 527 free-text responses from a 2025 survey of researchers who write code, most at U.S. universities. More than half of the accounts described running generated code, while automated tests and review by another person were rare. Respondents described one task each; this is survey evidence about those accounts, not a measure of all coding-agent use.
For consequential changes, seek stronger evidence than a successful run: use relevant automated tests where available, inspect the diff, and ask another person to review when appropriate. The exact checks depend on what the code is meant to do; no single check proves all aspects of correctness.
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Do you need to know how to code to use an AI coding agent?
You do not need to be a professional programmer to explain a goal or evaluate a visible result, but useful oversight still requires task-specific understanding. Microsoft Research’s 2025 study describes expertise as shifting toward managing context, rapidly evaluating code, and deciding when to move between AI-driven and manual work. Anthropic’s analysis also associated task-specific expertise with more successful sessions, including more precise directions and requests for verification. Neither source establishes that a non-coder can safely delegate any technical task.
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If you cannot judge whether a change is safe or correct, narrow the task and seek someone with relevant expertise to review it. The less able you are to recognize a failure, the less sensible it is to delegate a broad or consequential change without independent checks.
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What the evidence does—and does not—show
The sources point toward a consistent practical lesson: task framing, relevant context, reviewable steps, and feedback matter. They do not amount to a controlled comparison proving that this workflow is optimal, nor do they justify claims that AI now writes a fixed share of all code.
Anthropic’s session analysis does not observe whether generated code was ultimately used, and its classifications depend on model-based reading of transcripts. Microsoft Research analyzed a small, curated set of video, so its findings are qualitative rather than population-wide. The scientific-programming findings are from a preprint and survey responses. OpenAI’s account is a company case report.
Self-reported code-share figures also vary by category and respondent. JetBrains’ 2026 survey analysis, based on more than 15,000 professional developers and a May–July 2026 survey, reports averages of about 47% agent-generated, 38% AI-assisted, and 27% fully manual code. These are survey findings, not audited code telemetry; the categories sum to more than 100%, so they should not be read as mutually exclusive portions of a whole.
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Quick Recap
Sources
- OpenAI, “Harness engineering: leveraging Codex in an agent-first world”
- Microsoft Research, “Vibe coding: programming through conversation with artificial intelligence”
- Anthropic, “Agentic coding and persistent returns to expertise”
- Gabrielle O’Brien, Reed Milewicz, and Nasir Eisty, “How Researchers Use and Verify AI Coding Assistants: Tasks and Validation Practices in Scientific Programming”
- JetBrains Research, “How Much Code Do Developers Really Let Agents Write?”
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