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To get better code from AI, give the coding agent a clear outcome, the relevant project context and tools, small steps it can complete and review, and checks that show whether the result works. Then inspect the changes and iterate. Asking for code alone leaves the agent to guess what you need and gives you little evidence that its answer is correct.
How should you use an AI coding agent?
Treat the agent as a contributor working inside a defined process, not as a code vending machine. You decide what problem matters and what counts as a successful result; the agent can help investigate, implement, and test parts of that work.
Anthropic’s June 2026 analysis of roughly 400,000 Claude Code sessions found that people made about 70% of planning decisions and about 20% of execution decisions on average. In that study, planning included deciding what to do and what would count as done; execution included choices such as which files to change and which commands to run. These figures describe Anthropic’s analysis and attribution method, not all coding agents or software projects. Anthropic’s findings also do not establish whether generated code was ultimately used.
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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →1. Define the outcome and evidence of completion
Describe the user-visible or technical result you want, along with constraints that matter. Say how the agent can demonstrate completion: for example, the relevant test passes, a particular behavior appears in the running application, or an error no longer occurs.
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Keep the request focused. “Add a search field to the inventory page so users can filter by item name; preserve the existing layout; add a test for matching and no-match cases” gives a clearer target than “improve the inventory page.” The example is a framing pattern, not a guarantee that the implementation will be correct.
2. Provide the context and tools needed to act
Give the agent access to the relevant files, project conventions, documentation, and tools it needs to investigate the task. Mention important constraints—such as supported versions, APIs it must preserve, or areas it must not change. If the agent cannot see the relevant application behavior, logs, or test results, it may be forced to guess.
OpenAI’s account of its Codex workflow describes structuring the environment and exposing UI, logs, and metrics so agents could investigate and validate work. That is a company-reported practice, not proof that the same setup will produce the same results for every team. OpenAI’s description of its Codex workflow emphasizes that the working environment can affect whether an agent makes progress toward a high-level goal.
3. Split broad requests into reviewable stages
For a larger task, ask the agent to first inspect the relevant code and propose a plan. Review that plan before asking it to implement. Then inspect the changes, run suitable checks, and address any failures before moving on.
OpenAI describes a depth-first workflow organized around design, code, review, and test blocks. The benefit is practical: a misunderstanding discovered in a small, visible step is easier to correct than one buried in a large patch.
4. Check the result and feed back what happened
Do not treat a plausible explanation or a successful code-generation response as proof. Inspect the changed files, run tests suited to the task, and, where behavior matters, launch the application and exercise the affected path. Give the agent the actual failure output or observed behavior so it can make a targeted revision.
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Microsoft Research’s 2025 qualitative study of more than eight hours of curated video found that observed “vibe coding” sessions moved among prompting, rapid code evaluation and application testing, and manual editing. The study illustrates an iterative workflow; its small, curated sample is not a population-wide measurement. The researchers summarized the shift this way: “Critically, vibe coding does not eliminate the need for programming expertise but rather redistributes it toward context management, rapid code evaluation, and decisions about when to transition between AI-driven and manual manipulation of code.” Read the Microsoft Research study.
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How do you check code written by AI?
Match the check to the risk and the behavior the task is meant to change. A clean-looking patch is not the same as a verified one, and running code once does not establish that it handles important cases.
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- Review the changes: Confirm the agent edited the intended files, followed project conventions, and did not introduce unrelated changes.
- Run relevant automated tests: Use existing tests and ask for targeted tests when the task changes behavior. Read failures rather than accepting a claim that tests passed.
- Exercise the affected behavior: Run the application or command and try the success path as well as meaningful edge cases. Check logs or other available failure signals.
- Get another qualified review when the stakes warrant it: Changes involving security, money, sensitive data, or critical services deserve review appropriate to their consequences.
A September 2026 arXiv preprint examined 527 free-text responses from a 2025 survey of researchers who write code, most of them 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 evidence about that survey population, not a measure of every AI coding workflow. The preprint on researchers’ use and verification practices underscores why “it ran” should not be the only check.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do you need to know how to code to use a coding agent?
You do not have to be a professional programmer to give an agent a useful goal, notice whether the result matches your needs, or ask for a demonstration. But the less able you are to assess the code and its failure modes, the more important it is to keep the task bounded and use suitable tests or qualified review—especially when mistakes could cause harm.
Anthropic’s analysis associated task-specific expertise with more successful sessions. Its description includes users’ ability to frame directions precisely and ask the agent to verify its work; it does not establish that non-programmers can safely delegate any technical task. As Microsoft Research puts it, expertise shifts toward context management, evaluation, and judgment about when to edit manually. It does not simply disappear.
What AI coding statistics can—and cannot—tell you
Broad claims that AI now writes a fixed share of all code obscure how much results vary by developer, tool, language, and region. JetBrains’ August 2026 analysis of a globally representative survey of more than 15,000 professional developers reported average self-reported shares of about 47% agent-generated code, 38% AI-assisted code, and 27% fully manual code. The figures come from a survey question asked in May–July 2026, are self-reported, and sum to more than 100%; they are categories as reported, not mutually exclusive portions of a single total. They are not audited code telemetry. JetBrains explains its survey findings.
Together, the available studies and company accounts offer useful examples of planning, context, iteration, and verification. They do not constitute a controlled comparison proving one prompt or workflow is best for every project. The practical lesson is to make the work visible and reviewable, and to rely on evidence suited to the task rather than confidence in the agent’s answer.
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