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To enforce architectural contracts for coding agents, make intended behavior explicit, map the agent to relevant repository knowledge, and turn critical architecture boundaries into automated checks. A practical workflow separates the behavioral specification from the technical plan, breaks work into reviewable tasks, and validates each task against the right contract.
What an architectural contract should do
A coding agent needs more than a feature request. It needs to know what the software should do, where the relevant decisions and patterns live, and which boundaries must not be crossed. A specification captures intended behavior and success conditions; architectural contracts define the constraints that protect the system as that behavior is implemented.
Keep those ideas distinct from implementation prescriptions. A contract can require that one domain layer not depend directly on another, for example, without dictating a particular library or coding style. Make a choice mandatory only when it protects a real boundary or requirement; otherwise, preserve room for the agent and reviewer to choose an appropriate implementation.
GitHub describes a specification as a contract and shared source of truth for generating, testing, and validating code. Its article presents a staged workflow, not independent evidence that the approach improves outcomes in every project. GitHub’s Spec Kit overview
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Use a staged specification-to-implementation workflow
GitHub’s Spec Kit article organizes work into four phases. The sequence is useful because it separates intent from design decisions, then makes implementation small enough to inspect and validate.
1. Specify the behavior
Describe what is being built, why it matters, who will use it, the important user journeys, and how success will be recognized. State observable behavior and edge cases rather than asking the agent to infer the product goal from a vague feature label.
2. Plan the technical approach
Give the agent the relevant stack, architecture, constraints, existing patterns, and standards. This is where repository-specific direction belongs: for example, which layer owns a responsibility or which interfaces a change must preserve. The plan should apply the specification to the existing system without silently changing the requested behavior.
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3. Create focused tasks
Turn the plan into small work items that can be implemented and tested in isolation. A task should identify its scope and the expected result clearly enough that a reviewer can tell whether it is complete. Smaller units also make it easier to find which change introduced a failed check or missed requirement.
4. Implement with review checkpoints
Have the agent work through the tasks and review the resulting artifacts and code at checkpoints. Check that the specification still reflects the intended behavior, the plan respects local architecture, and the completed task satisfies its success conditions. GitHub also emphasizes revising the specification as understanding changes rather than treating it as immutable.
Give the agent a navigable map of the repository
Instructions are useful only if the agent can find and apply them. Keep durable context in versioned repository artifacts and provide a concise entry point that points to the relevant deeper material: architecture documentation, product specifications, plans, and established development conventions.
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OpenAI reports that a single large AGENTS.md file did not meet its context-management needs. Its published approach separates architecture, design documents, plans, and product specifications so agents can discover focused context instead of relying on one oversized instruction file. The exact layout is an example from OpenAI, not a universal directory structure. OpenAI’s account of harness engineering
Documentation quality can itself be treated as engineering work. OpenAI says it uses linters and CI jobs to check that its knowledge base remains structured, cross-linked, and current. A repository map is more dependable when stale links, missing structure, or broken conventions can be caught during routine checks.
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Turn important boundaries into mechanical checks
Write each architectural contract as an invariant that can be checked, then choose a validator suited to that invariant. OpenAI describes using custom linters and structural tests to enforce domain layers and permitted dependency edges. It also reports giving agents remediation guidance through error messages. That is a concrete practice from one engineering organization, not a mandatory blueprint for every codebase.
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- Dependency direction: use a structural test or linter to reject forbidden imports or dependency edges.
- API boundaries: use schema or contract checks when a change must preserve an interface.
- Behavior: use focused tests for the specified behavior, followed by relevant integration checks.
- Generated changes: run the project’s deterministic build and quality commands to detect compilation, test, or lint failures.
Make a failed check actionable. A message that names the violated rule and the permitted direction or location helps a person diagnose the issue and can help the agent revise its change. Avoid encoding incidental preferences as hard failures: a check should protect an actual contract, not merely make every implementation look alike.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Match validation to the claim being checked
Builds, tests, and lint tasks are validation mechanisms, not proof that the agent understood the request or that the architecture is sound. Review should connect each check back to the requirement it is intended to protect: behavioral tests to specified behavior, structural checks to dependency rules, and integration validation to interactions across components.
AWS describes coding agents as able to inspect development-environment context, reason about tasks, modify code, and trigger downstream build, test, or lint activities. That capability makes validation easier to incorporate into a workflow, but a passing command only establishes what that command actually checks. AWS Prescriptive Guidance on coding agents
For each task, record the relevant acceptance conditions and the checks that provide evidence for them. If a requirement has no corresponding check, it remains a review obligation rather than a mechanically enforced guarantee.
Choose the right level of formality
Specification-first work and informal prompt-first work involve trade-offs, not a proven performance ranking in the available sources. A staged process makes intent, small review units, architecture constraints, and validation traceability more explicit. Informal prompting may involve less upfront structure, but leaves more of those decisions implicit and harder to audit.
Likewise, strict and flexible contracts serve different purposes. Enforce boundaries that protect dependency direction, interfaces, or other architectural invariants. Leave implementation choices open when several options preserve the same invariant. The useful question is not whether to constrain the agent as much as possible, but which constraints are necessary to keep the change compatible with the system.
What the examples establish—and what they do not
GitHub’s September 2, 2025 article is vendor-authored guidance about Spec Kit and its specify–plan–tasks–implement workflow. OpenAI’s article is a first-party account of one organization’s repository context and automated architectural checks. AWS summarizes coding-agent patterns, while the SpecShip repository describes its own contract-first workflow and milestone gate. These sources document practices and examples; they do not provide an independent head-to-head evaluation or measured productivity and defect-reduction results.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe SpecShip repository should therefore be read as a description of that project’s approach, not independent evidence that the same workflow will produce particular results elsewhere.
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