AI coding agents typically build a feature by grounding the request in repository context, mapping relevant code and constraints, planning work to fit the task’s scope, editing through an interactive tool-use loop, and checking the result against project evidence and acceptance criteria. The exact sequence depends on the agent, repository, permissions, and task. Repository access and a detailed prompt alone do not guarantee a correct change; human review remains important.
1. Turn the feature request into concrete constraints
Before changing code, the agent needs to know what outcome is wanted and what boundaries matter. A useful request makes the intended behavior, affected users or interfaces, acceptance criteria, and constraints explicit. It should also identify unresolved product or design choices rather than silently treating them as settled.
- Expected behavior: What should users be able to do, and what should happen in relevant success and failure cases?
- Scope: Which interfaces or components may change, and what should remain untouched?
- Acceptance criteria: What observable behavior or checks would show the change meets the request?
- Open questions: Which decisions require clarification before implementation?
For an underspecified feature, the agent should surface assumptions or ask for clarification. Planning guidance from Visual Studio Code describes clarifying requirements and refining a plan as part of the process. A prompt is the starting specification, not proof that the agent has inferred the right product intent.
2. Give the agent usable context about the repository
Repository access is not the same as repository understanding. A feature can touch modules, tests, documentation, and conventions that are spread across many files. The agent needs to locate the relevant pieces, and its working context is finite: it may inspect files through tools, but it does not necessarily hold the entire repository in every model prompt. OpenAI’s technical explanation of the Codex agent loop describes conversation history and context-window management as part of the loop.
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Concise, maintained project guidance can help direct that inspection. OpenAI describes repository-local AGENTS.md files as a way to provide Codex with navigation guidance, test commands, and project practices in its documented environment (Introducing Codex). Visual Studio Code recommends focused project context such as architecture, product, and contribution documentation (Set up a context engineering flow in VS Code).
- Point to the modules and entry points that are likely relevant.
- Document local conventions, important dependencies, and commands for building and testing.
- Keep instructions focused enough to guide discovery instead of burying the task in unrelated detail.
- Review generated architecture or project notes: documentation can be stale or inaccurate.
Existing code may also contain inconsistent patterns. OpenAI’s account of its own engineering setup notes that Codex could replicate existing patterns and that drift still required attention (Harness engineering: leveraging Codex in an agent-first world). An agent can faithfully extend a poor precedent unless reviewers notice the problem.
3. Match the plan to the task’s scope and uncertainty
A small, well-specified change may need only a short sequence of edits and checks. A multi-component feature, migration, or substantial refactor benefits from a plan that makes the route to implementation reviewable before code changes begin. A useful plan identifies the goal, likely components, design choices, steps, dependencies, verification, and risks.
Visual Studio Code documents an iterative planning flow: project context informs a plan, the plan can be refined, and implementation follows (Set up a context engineering flow in VS Code). OpenAI’s ExecPlan guidance presents plans as design documents for complex features and significant refactors, and recommends staged milestones where appropriate (Using PLANS.md for multi-hour problem solving).
- Focused task, clear acceptance criteria: Keep the plan brief; avoid process that adds little value.
- Several dependent components: Break work into steps or milestones so assumptions and integration points are visible.
- Uncertain feasibility or requirements: Test the riskiest assumption early, potentially with a prototype or small exploratory implementation.
The plan should be something a person can inspect and revise, not a promise that every step is already correct. Planning is especially useful when later decisions depend on evidence learned during execution; OpenAI’s guidance on Goals in Codex distinguishes focused coding tasks from work whose next step depends on what is discovered.
4. Implement through connected edits and tool calls
After the plan is accepted, the agent can inspect relevant files, edit code, and use whatever tools its environment permits. In OpenAI’s description of Codex, the agent can read and edit files and run available test harnesses, linters, and type checkers; a turn can include multiple rounds of model inference and tool calls (Introducing Codex; Unrolling the Codex agent loop). These are descriptions of particular product workflows, not a guarantee that all coding agents have the same capabilities or access.
The work is often connected rather than local: a feature may require coordinated changes across an interface, implementation, tests, and documentation. A 2023 paper, CodePlan: Repository-level Coding using LLMs and Planning, frames interdependent repository edits as a planning problem. It helps explain why feature work across a codebase can be harder than completing a single isolated code fragment, but it is not a survey of current commercial agents.
Small, reviewable implementation steps make it easier to notice when an assumption fails or a change breaks an integration point. The agent’s actual ability to inspect files, run commands, or modify the repository depends on its product configuration and granted permissions.
5. Verify the change against both the codebase and the request
Verification should connect the implementation to the acceptance criteria, not stop at “the code changed.” Depending on the project and feature, useful evidence may include:
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- A regression test for the new behavior or a previously failing case.
- The relevant existing test suite, plus lint or type checks.
- A reproducible manual check, demonstration, or comparison with the stated acceptance criteria.
- Review of affected integration points, error paths, and documentation.
OpenAI’s harness engineering account describes a development loop that includes testing, validation, review, feedback handling, and recovery, including behavior validation in that organization’s engineered environment (Harness engineering: leveraging Codex in an agent-first world). It is an example from one deployment, not evidence that every agent automatically validates an application the same way.
Passing checks is evidence, not proof that every requirement or edge case is satisfied. Tests can miss untested behavior, and automated checks cannot settle every product or design judgment. A reviewer should compare results with the requested outcome and assess whether the evidence is meaningful for that feature.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Keep human review and acceptance in the loop
People remain responsible for choosing priorities, clarifying product intent, and deciding whether a result is acceptable. In OpenAI’s account of its harness, humans set direction, translate feedback into acceptance criteria, and validate outcomes (Harness engineering: leveraging Codex in an agent-first world). That is a description of the organization’s approach, not a measured rule about every engineering team.
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Review should consider more than whether a patch compiles: does it implement the intended behavior, fit the project’s architecture, handle relevant failure cases, and include adequate tests? For repository automation, GitHub’s documentation describes explicit permissions and safe outputs, with people able to review resulting issues, comments, and pull requests while retaining control over approvals and merges (About GitHub Agentic Workflows).
How workflow choices differ
Plan-first work, direct interactive execution, and ticket-driven orchestration are different ways to organize the same basic concerns. Choose based on the work and environment rather than assuming one workflow is best for every project.
| Workflow choice | Useful when | What to inspect |
|---|---|---|
| Brief plan, then focused edits | The change is narrow and acceptance criteria are clear. | Relevant context, permitted tools, and checks that cover the requested behavior. |
| Reviewable plan before implementation | The feature spans components, has dependencies, or contains uncertain design choices. | Assumptions, milestones, risks, and verification steps before edits begin. |
| Issue- or ticket-driven orchestration | Work needs to be coordinated across tasks or tracked with dependencies and review. | Task boundaries, dependencies, permissions, and how people review proposed outputs. |
OpenAI describes Symphony as a ticket-oriented orchestration approach used in its own setting (An open-source spec for Codex orchestration: Symphony). Its account reports a 500% increase in landed pull requests on some teams; this is an OpenAI-reported outcome, with no controlled comparison established in that account, and should not be treated as a general expected productivity gain. The sources reviewed do not establish that one vendor or workflow outperforms the others across task scope, context, permissions, planning, verification, and review.
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