AI coding agents build features by combining a clear description of the desired behavior with usable repository context, then inspecting the project, planning work when needed, editing files through available tools, and checking the result against tests and acceptance criteria. The exact workflow depends on the agent, task, repository, and permissions. Access to a codebase is not the same as understanding it, and a prompt or passing test suite does not guarantee a correct feature.
1. Turn the feature request into constraints
Before changing code, an agent needs to know what should happen and what must remain unchanged. A useful request identifies the expected behavior, affected users or interfaces, boundaries, and acceptance criteria. For a web feature, that might mean specifying which screen changes, what a user can do, and how errors or empty states should behave.
Unresolved design choices should be surfaced rather than silently converted into assumptions. The agent can ask questions or propose assumptions for review. Microsoft’s VS Code context-engineering guidance describes clarification and iterative plan refinement as useful parts of planning.
2. Give the agent usable repository context
Repository access lets an agent inspect files; it does not guarantee that it will infer every project convention or identify the right implementation path. Relevant context includes architecture notes, product behavior, contribution instructions, tests, and the commands used to validate changes.
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OpenAI says Codex can follow repository-local AGENTS.md files that describe how to navigate a project, run tests, and follow its practices. VS Code recommends curated project instructions and documentation, while advising that initial context stay focused. Generated documentation can be stale or inaccurate, so review it before relying on it as authoritative guidance.
Context is also finite. An agent may inspect files using tools, but that does not mean the entire repository is present in every model prompt. OpenAI’s explanation of the Codex agent loop describes conversation history being incorporated into later prompts and context-window management as part of the process.
3. Decide how much planning the task needs
A small, well-bounded change may need only a short sequence of steps. A feature spanning components, a migration, or a significant refactor benefits from a plan people can inspect before implementation. A useful plan connects the requested behavior to design choices, affected components, implementation steps, verification, and risks or dependencies.
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VS Code documents a flow in which project context informs an implementation plan, which can be refined before code generation. OpenAI’s ExecPlan guide recommends a design-document-style plan for complex features and significant refactors. For uncertain or challenging requirements, it suggests milestones and prototypes or toy implementations to test feasibility early. That is not a reason to write a long plan for every task: planning effort should track the scope and uncertainty.
4. Implement through connected edits and tool calls
After the plan is understood, an agent can edit the files and use the tools allowed by its environment. OpenAI’s description of Codex says it can read and edit files and run available test harnesses, linters, and type checkers in its described product environment. Its agent-loop explanation describes a turn as potentially containing multiple rounds of model inference and tool calls; the main output can be modified code rather than chat text. These are documented Codex capabilities, not a guarantee that every coding agent has the same tools or permissions.
Feature work often crosses module boundaries: a user-visible change may require coordinated updates to application logic, interfaces, tests, or documentation. The 2023 paper CodePlan: Repository-level Coding using LLMs and Planning frames interdependent repository edits as a planning problem. It helps explain why work across an established codebase is different from completing one isolated snippet; it is not a survey of current agent products.
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5. Verify the change against both code and requirements
Verification should combine project checks with the original acceptance criteria. Depending on the change and setup, useful evidence may include a regression test, an existing test suite, a linter, a type check, a reproduction, or a demonstration of the changed behavior. The right checks are those that exercise the feature’s relevant behavior, not simply every available command.
OpenAI’s harness engineering account describes a development loop that includes testing, validation, review, feedback handling, and recovery, along with validating application behavior in its own engineered environment. Those are examples from a particular deployment. Passing checks provide evidence, but cannot establish that every requirement, edge case, or user expectation has been met.
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Review also matters in automated repository workflows. GitHub’s documentation for Agentic Workflows describes explicit permissions and safe outputs; resulting issues, comments, and pull requests can be reviewed by people, who retain control over approvals and merges.
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6. Keep human judgment in the loop
People remain responsible for choosing priorities, resolving product questions, setting acceptance criteria, and deciding whether the evidence is adequate. In OpenAI’s harness account, humans prioritize work, translate feedback into acceptance criteria, and validate outcomes. That describes the role in that organization’s workflow, rather than a measured rule about every engineering team.
Existing code can also contain inconsistent or outdated patterns. OpenAI’s account says its Codex system sometimes replicated existing patterns and that drift required attention. A reviewer should therefore assess whether a proposed change fits the project’s intended conventions, not merely whether it resembles nearby code.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a workflow that fits the work
Plan-first work, direct interactive execution, and issue-driven orchestration are different ways to organize the same broad chain of understanding, editing, and verification. Choose based on the task and the controls the project needs.
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| Workflow | When it fits | What to inspect |
|---|---|---|
| Direct interactive execution | A focused change with clear requirements and a narrow verification path. | Whether the agent has the necessary repository context, tools, and permission to make the change; inspect the resulting diff and checks. |
| Plan-first execution | A multi-component feature, migration, refactor, or task with substantial uncertainty. | Whether the plan identifies affected components, dependencies, verification, and assumptions—and whether it can be revised before edits begin. |
| Issue-driven orchestration | Work organized around tickets, dependencies, and reviewable outputs across a team or workflow. | How permissions are scoped, how dependencies are handled, and where people review and approve results. OpenAI describes Symphony as a ticket-oriented approach used in its own setting. |
Across all three, evaluate repository instructions and their freshness, the opportunity for plan review, tool access and approval boundaries, meaningful verification evidence, and the human review point. Product configurations vary. The cited sources do not establish through a controlled comparison that one vendor or workflow is best across these dimensions.
OpenAI reports a 500% increase in landed pull requests on some teams in its Symphony account. This is a vendor-reported result, with no controlled comparison established in that account; it should not be treated as a typical or causal productivity gain for other teams.
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