Spec-Driven Development for AI Coding Agents

A developer asks the AI Coding Agent to fix one small feature, but the agent changes unrelated files, forgets an edge case, and leaves the team debating whether the result is complete.

The fastest solution is to use Spec-Driven Development: define stable project constraints, write a testable Specification, generate a dependency-aware plan and task list, then require evidence against the original requirements before accepting code.

This guide is for:

• Individual developers who repeatedly correct AI-generated code.
• Technical leads introducing AI programming into an existing team workflow.
• Engineering teams that need an auditable link between requirements, code changes, tests, and acceptance decisions.

The operating model

Spec-Driven Development is not “write a longer prompt before coding.” It is a controlled sequence in which intent becomes a set of artifacts that can be reviewed independently:

1. Project constraints define what must not drift.
2. A Specification defines observable behavior.
3. A design plan explains how the behavior fits the codebase.
4. A task list limits the scope of each implementation round.
5. Validation evidence proves whether the result matches the Specification.
6. Version control records how requirements, implementation, and tests changed together.

The current official Spec Kit workflow follows the same general model: constitution, specification, plan, tasks, and implementation, with optional clarification, checklist, analysis, and convergence steps. (github.com)

The important distinction is that each stage has a different question. The specification asks what must happen. The plan asks how the repository should support it. The task list asks what can be changed and verified in one controlled unit.

Why prompt-first development breaks down

A prompt can describe an intention, but it usually does not preserve the boundaries around that intention. Four problems appear repeatedly.

Context drift: The agent receives a large repository, a long conversation, and several loosely related instructions. It may optimize for the latest sentence rather than the original requirement.

Hidden coupling: A request that sounds local may affect database schemas, API contracts, permissions, tests, deployment scripts, or generated files. Without an impact analysis, the agent discovers these dependencies during implementation.

Unclear completion: “The feature works” is not an acceptance rule. A reviewer still needs to know which input cases, failure paths, security conditions, and regression checks define completion.

Unsafe correction loops: When the first implementation is wrong, developers often add another prompt to patch it. That can make the code match the conversation while moving further away from the intended behavior.

A Specification prevents these issues only when it is treated as a versioned source of intent rather than disposable prompt text.

Project constraints first

Before describing a feature, create a compact project constitution or engineering contract. It should contain rules that are stable across multiple tasks and features.

A useful constraint file records:

• Approved languages, frameworks, package managers, and runtime targets.
• Repository layout and ownership boundaries.
• Naming, formatting, linting, and test conventions.
• Files or directories that the agent must not modify.
• Security boundaries, secrets-handling rules, and data-access restrictions.
• Required commands for local validation and continuous integration.
• The definition of done for pull requests or release candidates.
• Rules for migrations, generated files, dependency changes, and public API changes.

The goal is not to document every preference. The goal is to remove repeated explanations from future tasks. A rule belongs in the shared constraints only when it is stable enough to apply more than once.

For example, “Use the existing validation library for all public request objects” is a reusable rule. “Use this helper in the checkout endpoint” belongs in the feature plan because it may not apply elsewhere.

The constraint layer also defines the agent’s authority. A practical boundary can state:

• The agent may modify files inside the feature module.
• The agent may add tests in the matching test directory.
• The agent may not change authentication middleware without explicit approval.
• The agent may not upgrade dependencies while implementing a feature.
• The agent must stop when a required interface is missing or ambiguous.

That last rule matters. A productive AI Coding Agent is not one that always produces a patch. It is one that knows when the available information is insufficient.

Specification design

A good Specification describes behavior in terms that a test, reviewer, or product owner can judge. It should not begin with a preferred class name or an assumed implementation.

A reliable structure is:

Specification section	What it should define	Example evidence
User outcome	The result the user needs	A user can save a draft without publishing it
Inputs	Accepted values, required fields, and boundaries	Missing title is rejected with a documented error
Outputs	Response shape, state change, or visible result	The draft appears in the author’s workspace
Failure behavior	Errors, retries, permissions, and unavailable dependencies	A non-owner receives a forbidden response
Non-functional rules	Security, compatibility, audit, accessibility, or operational constraints	No secret is written to logs
Acceptance evidence	How completion will be checked	Automated test, API example, and manual review

Each important requirement should be expressed as an observable statement. “The system should be fast” is not sufficiently testable. “The endpoint must preserve the existing pagination contract” is more useful because the contract can be inspected and tested.

The Specification should also separate must-have behavior from implementation freedom. If the requirement is that a user can resume an unfinished upload, the agent can choose the internal state representation unless the repository already fixes that choice. Over-specifying internal details can reduce the agent’s ability to fit the change into the existing architecture.

The specification quality test

A Specification is ready for planning when a reviewer can answer these questions without asking for the original conversation:

• What behavior is changing?
• What behavior must remain unchanged?
• Which users, services, or interfaces are affected?
• What happens for valid, invalid, empty, duplicate, unauthorized, and unavailable inputs?
• What evidence will prove each requirement?
• What is explicitly outside the feature scope?

If any answer depends on “the agent will understand what we mean,” the requirement is not ready.

Plan and task boundaries

Once the Specification is reviewable, ask the AI Coding Agent to produce a design plan before asking for code. The plan should identify affected components, data flow, interfaces, dependencies, migration concerns, and validation strategy.

The plan is not a second copy of the requirement. It is a bridge between behavior and repository structure.

A useful plan contains:

• Existing modules and entry points that will be reused.
• New components or files that are required.
• Data-model and API changes.
• Dependency and permission impacts.
• Test layers affected by the change.
• Risks, unresolved questions, and alternatives rejected.
• A mapping from each design decision back to one or more requirements.

Official Spec Kit templates follow this separation by producing planning artifacts and then handing the work to task generation rather than immediately implementing the plan. (github.com)

The task list should then turn the plan into units that can be implemented and verified independently.

Task property	Weak task	Strong task
Scope	“Implement draft support”	“Add draft status to the existing article state model”
Location	No file or interface boundary	Identifies the model, serializer, and related tests
Dependency	Hidden	States that the migration must precede API updates
Validation	“Check that it works”	Runs migration tests and verifies old published records
Completion	Subjective	Includes a checkbox and evidence requirement

A task is too large when it crosses several unstable modules, requires multiple unreviewed design decisions, or cannot be validated without the entire feature being complete. A task is too small when its isolation creates overhead without improving reviewability.

Parallel markers should be used carefully. Two tasks can run in parallel only when they do not depend on the same unresolved decision or repeatedly modify the same file. Spec Kit’s own documentation emphasizes bounded slices and independent feature artifacts to keep context manageable. (github.com)

Controlled implementation rounds

Implementation should proceed one task at a time, or one clearly independent group at a time. Each round should provide the agent with only the information needed for the current task:

• The relevant part of the Specification.
• The approved plan section.
• The current task and its prerequisites.
• The affected files or interfaces.
• The required validation commands.
• The project constraints that apply.

Before modifying code, require a short plan containing:

1. Files the agent expects to change.
2. Existing behavior it intends to preserve.
3. Assumptions that could invalidate the task.
4. The validation it will run after editing.

After modifying code, require a completion record:

• Files changed.
• Requirement or task IDs addressed.
• Tests and checks executed.
• Results, including failures.
• Remaining uncertainty.
• Any deviation from the approved plan.

This creates a reviewable handoff instead of a vague statement that the agent “finished.”

A controlled round should stop when:

• A required dependency is missing.
• The task requires changing an excluded area.
• The implementation contradicts the Specification.
• A test exposes an unrecorded behavior decision.
• The agent needs a new permission, package, schema, or public interface.

The correct response is not another corrective prompt. Update the appropriate artifact, regenerate affected tasks, and continue from a known state.

Acceptance and rollback

Acceptance should map back to the original Specification rather than judging the patch as a standalone object.

A compact acceptance matrix may look like this:

Requirement	Implementation location	Automated check	Manual check	Status
Owners can save drafts	Article service and API	Service test and API test	Inspect draft in workspace	Pending
Published content remains visible	Query and serializer	Regression test	Compare existing response shape	Pending
Non-owners cannot edit	Authorization policy	Permission test	Review audit event	Pending

The matrix prevents a common failure: passing a large test suite while leaving one important requirement unverified.

Use multiple evidence types when the requirement crosses technical and user-facing boundaries:

• Unit tests for local rules.
• Integration tests for module boundaries.
• Contract tests for API or event formats.
• Static analysis for type, security, and style constraints.
• Example requests and responses for interface review.
• Manual checks for visual behavior, workflow clarity, or operational setup.

The acceptance process should also define rollback. If a task fails, return to the smallest artifact that is wrong:

• If the requirement is ambiguous, revise the Specification.
• If the architecture does not fit, revise the plan.
• If the work is too broad, split the tasks.
• If the code is wrong but the artifacts are sound, revert the implementation task.
• If the environment is unreliable, reset it before judging the code.

The point is to avoid accumulating temporary fixes that hide a flawed requirement.

Versioned change control

Requirements change. The safe response is not to edit the code first and update the documentation later.

A controlled change sequence is:

1. Record the requested behavior change.
2. Identify affected Specification sections.
3. Mark requirements added, removed, or modified.
4. Review impact on interfaces, data, permissions, tests, and deployment.
5. Update the plan and task list.
6. Re-run consistency checks.
7. Implement only the new task set.
8. Re-run acceptance against the revised Specification.

GitHub Spec Kit includes optional analysis and convergence concepts for checking cross-artifact alignment and identifying remaining work after implementation. Its workflow documentation also describes persisted state and resume behavior for workflows that pause or fail. (github.com)

A useful repository layout can keep the relationship visible:

.specify/
  memory/
    constitution.md
  specs/
    001-draft-saving/
      spec.md
      plan.md
      tasks.md
      checklist.md
      acceptance.md

The exact directory structure may differ by tool or integration, so the project should follow the current official setup instructions rather than copying an old command list. Spec Kit’s documentation notes that initialization creates the relevant command files and project structures for the selected coding agent. (github.com)

Execution checklist

Use this checklist before allowing an AI Coding Agent to modify production code:

• [ ] The project constraints identify the approved stack and validation commands.
• [ ] The task states what is in scope and what is excluded.
• [ ] The Specification defines observable behavior rather than only implementation ideas.
• [ ] Valid, invalid, unauthorized, duplicate, and dependency-failure cases are covered where relevant.
• [ ] The plan identifies affected modules, interfaces, data, and tests.
• [ ] Every task has a bounded scope and explicit prerequisites.
• [ ] Parallel tasks do not share unresolved design decisions.
• [ ] The agent must state its intended files and assumptions before editing.
• [ ] The implementation result records changed files and executed checks.
• [ ] Acceptance evidence maps back to individual requirements.
• [ ] A failed task can be reverted without discarding unrelated work.
• [ ] Specification changes are committed before the corresponding code changes.
• [ ] The final review checks both behavior and artifact consistency.

This checklist is more valuable than a generic “review the code” instruction because it creates explicit stop conditions.

Environment fit

Spec-Driven Development reduces ambiguity, but it does not repair a weak execution environment. The agent still needs a reproducible repository, the correct runtime, reliable test dependencies, and a reset path when the workspace becomes contaminated.

A local laptop may be appropriate when one developer owns the repository and the target environment is already available. A shared CI runner may be better for repeatable checks, but it can become slow or difficult to inspect interactively. A temporary remote development machine can be useful when the team needs an isolated environment for a short implementation cycle, cross-platform testing, or a clean handoff between developers.

Environment	Best fit	Common limitation
Local workstation	Fast iteration on a familiar stack	Results may depend on unrecorded local state
Shared CI runner	Repeatable automated validation	Interactive debugging and environment inspection can be limited
Temporary remote Mac environment	Apple-platform builds, isolated testing, and short-lived team access	Physical-device access and long-term heavy workloads may not fit
Dedicated project machine	Stable long-running development	Higher ownership and maintenance burden

For teams evaluating a remote Mac workflow, the decision should be based on the actual toolchain, reset requirements, build access, and test duration. Zutcloud’s Mac rental pricing information can be reviewed after the specification and environment checklist are complete, rather than using infrastructure selection as a substitute for process design.

If the project needs a cloud-based development environment, the team should document how source code is mounted, how credentials are injected, how the machine is reset, and which validation commands run in a clean session. The Zutcloud help center is the appropriate place to confirm operational details before adopting a remote workflow.

FAQ

How should a developer start with Spec-Driven Development?

Start with a small feature and write the project constraints before describing the feature itself. Record the approved stack, directory boundaries, security rules, validation commands, and definition of done. Then create a short Specification with user behavior, inputs, outputs, failure cases, and acceptance checks. Do not ask the agent to modify code until these boundaries are reviewable.

How detailed must a software specification be before an AI Coding Agent can execute it?

The Specification should be detailed enough for two independent reviewers to reach the same pass or fail decision. It does not need to prescribe every function or class. It must define observable behavior, input and output conditions, error handling, security constraints, affected interfaces, and the evidence required for acceptance. Implementation choices belong in the plan unless they are fixed project constraints.

How can an AI Coding Agent split work from a Specification?

Ask the agent to produce a dependency-aware plan first, then convert each design area into a task with one target, explicit prerequisites, affected files or interfaces, and a validation command. Keep tasks small enough to review independently. Mark parallel work only when tasks do not share unstable files, shared schemas, or unresolved design decisions.

How do teams prevent code from drifting after a Specification changes?

Treat the Specification as a versioned engineering artifact. When a requirement changes, update the specification first, record the impact on the plan and tasks, and identify tests that must change. Stop implementation until the affected artifacts are aligned. This prevents the agent from preserving obsolete behavior through temporary prompts or isolated patches.

The practical comparison is between an ad hoc local setup and a controlled Mac-based environment. A local machine can carry undocumented dependencies, stale build artifacts, and user-specific permissions; a shared runner can hide failures behind queue time or limited interactive access. For Apple-focused work, renting a Zutcloud Mac environment may offer a cleaner temporary workspace, easier separation between experiments, and a more predictable handoff, while self-purchasing remains the better choice for long-term heavy use or workflows that require permanent physical-device access. Review the Zutcloud Mac rental options only after confirming that the project’s build, test, and reset requirements match a remote environment.

Further reading

• Explore a Multi-Agent Coding Workflow with Claude Code
• Designing a File System for AI Agents
• Build a Parallel AI Coding Workflow with Orca