How to give an AI coding agent reliable domain knowledge

A general-purpose coding agent can write a plausible answer while missing the one detail that matters in your stack: the API that changed last month, the repository boundary nobody should cross, or the project convention that keeps production stable. The fix is not always a longer prompt. A better pattern is to give the agent small, reusable domain guidance and a checkable definition of done.

This approach is useful whether you work in Android Studio, VS Code, Claude Code, Cursor, or another agent host. The names and file locations vary, but the design problem is the same: supply the right context at the right time, then verify the result.

1. Separate project rules from domain procedures

Start with two layers. The first is a short repository contract: what the project is, how it is organized, which commands build and test it, what files are off-limits, and what “done” means. JetBrains’ agent behavior documentation describes instruction files such as AGENTS.md and CLAUDE.md as guidance that travels with the repository and can be shared across tools. It contrasts those files with IDE-only project rules.

Keep this layer stable and operational. It should answer questions an agent needs on every task: “Where is the domain logic?”, “How do I run the checks?”, and “What must I not edit?” Do not turn it into a handbook. If a rule is needed only for one workflow, put it in a separate skill or procedure.

The second layer is domain knowledge: a focused recipe for a recurring task such as an Android navigation migration, a database schema change, or a release checklist. A skill should have a clear trigger, a short sequence of actions, and explicit validation. This keeps the agent’s default context small while making specialized help available when the task calls for it.

2. Write a skill around a knowledge gap

A good skill exists because the model repeatedly gets something wrong or wastes time rediscovering it. Android’s skills philosophy offers a useful test: official skills are created for verifiable gaps in current models, especially when APIs are changing or a team uses a customized architecture. The same post also warns developers to download skills from reputable sources rather than trusting large collections of untested, possibly malicious instructions.

For example, a small project skill could look like this:

---
name: navigation-upgrade
description: Use when changing the app's Navigation 3 setup or upgrading its related APIs.
---

Before editing:
1. Inspect the current dependency versions and existing navigation tests.
2. Read the project's navigation conventions.
3. Confirm the target API in the approved platform documentation.

While editing:
- Make the smallest vertical change that satisfies the requested behavior.
- Do not replace the navigation architecture without approval.
- Preserve existing deep-link and back-stack behavior unless the task says otherwise.

Done means:
- The focused tests pass.
- The relevant build or lint command passes.
- The response names changed files, checks run, and unresolved risks.

The example is intentionally narrow. It does not paste an entire framework manual into every prompt. It tells the agent when the procedure applies, what to inspect, which boundaries matter, and how to demonstrate completion. Replace the example’s platform-specific checks with the commands and conventions that are real in your repository.

3. Make the agent inspect before it acts

Coding agents are useful because they can combine a request with environment context, reason over that context, and execute actions such as edits, tests, and builds. That is the workflow described in AWS Prescriptive Guidance. Your instructions should make those stages visible.

A practical request has four parts:

1. Context: identify the feature, the relevant directories, and the constraints.
2. Plan: ask the agent to list the files and checks it expects to use before editing.
3. Change: ask for the smallest implementation that satisfies the acceptance criteria.
4. Evidence: require the exact tests, lint, build, screenshots, or manual checks that support the result.

This prevents a common failure mode: an agent starts coding from the user’s sentence, discovers the repository’s actual architecture late, and then compensates with a broad rewrite. Inspection is not ceremony; it is how the agent obtains the environment context its decisions depend on.

4. Give the agent a tight feedback loop

Domain guidance tells an agent how your project works, but executable checks tell it whether the change works. VS Code’s current TDD guide demonstrates a useful separation: a red phase writes a failing test, a green phase implements the minimum code needed to pass, and a refactor phase improves structure while keeping the tests green. It also shows how custom instructions can apply testing conventions selectively.

You do not need three elaborate agents to borrow the idea. For a small task, ask for one failing or characterization test first. Then let the implementation proceed only after the expected behavior is concrete. Finish with a focused test run and the project’s normal quality gates. If the agent cannot make the check pass, that is valuable information about the requirement or the environment—not a reason to hide the failure in a more confident summary.

5. Review and retire guidance

Treat a skill as versioned engineering infrastructure. Review it when the framework changes, when the agent makes a repeatable mistake, or when a check becomes noisy. Android’s skills guidance explicitly frames skills as candidates for deprecation: as models improve, some instructions become unnecessary, while new APIs create fresh gaps.

A lightweight maintenance loop works well:

• Keep one example task that exercises the skill.
• Record the expected files and validation commands.
• Run it after changing the skill or upgrading the model.
• Remove instructions the model no longer needs.
• Keep security-sensitive actions opt-in and reviewable.

The goal is not to make the agent sound more expert. It is to make correct behavior easier to reproduce. A short repository contract, a narrowly triggered domain procedure, and an executable definition of done give an AI coding agent something better than an enormous prompt: a path from context to change to evidence.