Give the agent one bounded outcome, a short map to the repository’s authoritative guidance, and observable completion criteria. For a large change, have it inspect and propose a plan before editing; then implement in reviewable steps and check the results. The goal is not to load the agent with everything in the codebase, but to help it find the right information and verify its work.
How do I keep an AI coding agent focused on a large codebase?
Use a repeatable loop: define the task, point to relevant repository knowledge, plan substantial changes, implement in small slices, and verify each slice. The task brief should say what outcome matters and how you will recognize it—not dictate every line of code. Give the agent enough context to orient itself, then let it inspect the code and supporting documentation it needs.
- Define the outcome. Describe the requested behavior or fix, why it matters, the boundaries of the change, and what must remain unchanged.
- Point to useful context. Name known relevant files, components, examples, or documentation. If you do not know the right paths, ask the agent to map them before implementation.
- Request a plan for substantial work. For changes spanning multiple files or packages, ask for an inspection and plan without edits. Review its assumptions, affected interfaces, tests, and architectural constraints.
- Implement in reviewable steps. Approve the plan, then have the agent make a coherent slice at a time. Check intermediate changes before broadening the work.
- Make completion observable. Specify the build, test, runtime behavior, or other evidence that will show whether the change meets the acceptance criteria.
OpenAI recommends issue-like prompts that include concrete repository references such as paths, component names, relevant diffs, and documentation snippets. Anthropic’s Claude Code guidance similarly recommends an outcome and acceptance criteria. These are useful prompt-design recommendations, not a guarantee that an agent will infer every unstated requirement.
What should go in AGENTS.md?
Use a root AGENTS.md as an entry point to durable, high-value guidance—not as a copy of the whole codebase or a complete manual. OpenAI’s February 2026 engineering account describes using a short file as a table of contents that directs Codex to structured repository documentation. The team reported that a monolithic instruction file crowded out task and code context and became difficult to maintain and verify; that is its engineering experience, not a universal comparison of file strategies.
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Include information an agent needs to orient and avoid common mistakes, such as:
- Where authoritative architecture, domain, product, and testing documentation lives.
- Build and test commands that work in the current repository.
- Conventions the team actually follows, plus important boundaries and known quirks.
- Examples or paths showing the preferred implementation pattern.
Move detailed specifications, decision logs, execution plans, and domain-specific material into linked documentation where they can be consulted when relevant. Keep those documents discoverable through the repository or the tools the agent can actually use.
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Choose the file structure for maintenance and context cost
| Approach | Useful when | Trade-off to manage |
|---|---|---|
| One root instruction file | The repository is small enough that its durable rules and orientation fit in a concise entry point. | As the file grows, always-loaded material can compete with task-specific context and become harder to validate. |
| Short index plus linked documentation | Different tasks need different architecture, domain, testing, or product details. | The index and linked documents must remain accurate, findable, and accessible to the agent. |
There is no broadly validated universal instruction-file size. Anthropic Help offers an under-roughly-200-line suggestion as its own practical heuristic, not a cross-tool standard. Prefer a length that preserves useful signal and is easy for the team to maintain. Remove stale history, details already obvious from the tree, full manuals available in source, and aspirational rules the team does not follow. Review the file when conventions change or repeated mistakes reveal a missing or misleading instruction.
When should I ask the agent to plan first?
Ask for a plan when the cost of a wrong turn is meaningful: for example, a change crosses files or packages, alters interfaces, touches architectural boundaries, or has dependencies that are not obvious. Review before implementation for missing components, impacted tests, compatibility requirements, and assumptions that need confirmation. Once the approach is sound, authorize implementation in small steps and inspect the results as they arrive.
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OpenAI recommends using Ask Mode before Code Mode for large changes; Anthropic’s Claude Code guidance recommends Plan Mode for work touching more than a couple of files. Those labels belong to specific products. The general practice is to separate investigation and planning from editing when doing so makes the work easier to review. For a small, well-understood change, a plan-first phase may add unnecessary overhead.
How do I stop context from drifting?
The active context includes more than the initial prompt and repository instructions. Long histories, irrelevant tool results, and tool descriptions can all take space that would otherwise be available for the current task.
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- Keep unrelated implementation tasks in separate sessions instead of letting old discussion blur the current goal.
- When changing tasks, carry over only durable repository guidance and a concise brief for the new work.
- If the agent supports compaction or context editing, retain decisions, constraints, current state, and next steps while removing obsolete output.
- For agents with many tools, use on-demand tool discovery or programmatic calling where supported, rather than loading every tool description by default.
Anthropic distinguishes on-demand tool search, programmatic calling, prompt caching, and context editing as options for different kinds of context pressure. They are not interchangeable fixes: choose based on whether the problem is tool-description overhead, repeated inputs, or accumulated conversation history.
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Give the agent the relevant feedback loop and require a report of the checks it ran and their results. Match the check to the change: a bug fix needs a reproducible case or targeted test; a build-related change needs the relevant build; a UI change needs inspectable runtime behavior. State the expected behavior, error text, or other observable result when you know it.
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OpenAI’s February 2026 account describes using per-worktree app instances, browser inspection, logs, metrics, and mechanical checks for documentation structure and architectural invariants. These are examples from one organization. A passing test supports only the behavior that test covers; it does not by itself establish that every requirement or architectural rule has been satisfied.
Do context files make an agent more accurate?
Not reliably in every task. A 2026 arXiv preprint by Prakhar Khatri reports 288 evaluated runs across 17 tasks from three repositories. It found no measurable correctness effect from context-injection strategy within the reported equivalence bounds: no more than 10 percentage points for Claude and 15 percentage points for Codex in the abstract. This is a bounded experiment involving a limited set of tasks, repositories, and agent families—not evidence that repository guidance never helps or that more context is always useful. Treat context files as navigation and instruction aids, and evaluate them against your own work.
Reusable prompt checklist
Before sending a substantial coding task, check that the brief contains the essentials:
Quick Recap
- Outcome and reason: What should change, and why?
- Scope and exclusions: What is included, and what should remain untouched?
- Repository context: Which paths, examples, or authoritative documents matter—or should the agent map them first?
- Constraints: What compatibility, architecture, or team conventions must be respected?
- Acceptance criteria: What observable behavior means the task is complete?
- Checks: Which exact tests, build commands, logs, or runtime behaviors should be examined?
- Planning request: For a complex change, should the agent inspect and propose a plan without editing first?
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