Spec-Driven Development Complete Guide: How to Drive an AI Coding Agent with Software Specifications

A familiar symptom appears after a few AI coding sessions: the feature looks nearly complete, but each correction breaks another behavior, changes files outside the intended scope, or produces a new interpretation of the original request.

The fastest fix is to use Spec-Driven Development for an AI Coding Agent: turn intent into project constraints, a testable Specification, a reviewed design, independent tasks, and acceptance evidence before allowing broad code changes.

Who should use this workflow

This guide is for individual developers who repeatedly correct generated code, technical leads introducing AI-assisted development into a team, and engineering groups that need an audit trail for agent-produced changes.

It is not necessary for a one-line configuration edit. It becomes valuable when a feature crosses several modules, affects permissions or data, or must be reviewed by someone other than the person who wrote the initial request.

The decision in one view

A useful Spec-Driven Development process does not mean writing the longest possible requirements document. It means preserving the decision boundary at every stage:

Intent → constraints → Specification → design → tasks → implementation → evidence

The AI Coding Agent can generate artifacts and code, but it should not silently decide what is protected, what “complete” means, or which requirements can be ignored.

Development situation	Recommended control level	Required artifacts	Reason
Small, isolated change in one file	Lightweight	Short requirement, diff, one verification command	A full workflow may cost more time than the change
Feature touching several modules	Structured	Constitution, Specification, plan, tasks, tests	Cross-module assumptions need visible boundaries
Authentication, billing, data migration, or permissions	Strict	Versioned Specification, threat checks, review gates, automated tests, manual acceptance	A plausible implementation can still violate security or business rules
Team-owned feature with future maintenance	Full	All core artifacts plus change history and traceability	Future developers need to know why the code behaves this way

The process should scale with uncertainty, not with the amount of text in the initial prompt. A short feature with unclear permissions deserves more control than a large but well-understood refactor.

Stage one: establish project constraints

Before asking an agent to design a feature, create a stable project-level constraint file. In the Spec Kit model, the project constitution stores governing principles that should influence planning and implementation. The official Spec Kit overview describes this layer alongside the Specification, planning, task, analysis, and implementation stages.

The constraint file should record:

• Supported languages, frameworks, package managers, and runtime versions.
• Directory and naming conventions.
• Commands for formatting, linting, unit tests, integration tests, and builds.
• Authentication, authorization, secret-handling, and data-retention boundaries.
• Files or directories the agent must not modify without explicit approval.
• Public interfaces that require backward compatibility.
• Rules for migrations, logging, error handling, and observability.
• The definition of done for the repository.

The goal is to avoid repeating the same warnings in every prompt. If a repository must never store credentials in source files, that rule belongs in the project constraints. If generated code must pass a particular test command, that command belongs there too.

A constraint is useful only when it can influence a decision. “Write maintainable code” is too vague. “Do not add a new dependency when an existing package provides the same capability” is reviewable. “All externally supplied identifiers must be validated before database access” is testable.

Stable constraints versus feature requirements

Project constraints should change slowly. Feature requirements describe what the next capability must do. Mixing both creates two common failures:

1. The agent treats a feature-specific exception as a permanent project rule.
2. The team edits a broad rule to make one feature easier, weakening future work.

Keep the layers separate. A Specification may say that an administrator can export a report. The project constraints should still define how authorization is checked, where export files are stored, and which audit event is required.

Stage two: write a testable Specification

A Specification should describe observable behavior rather than implementation preference. The official Spec Kit repository separates the command that defines requirements and user stories from the later command that creates a technical implementation plan. That separation matters: it prevents the first technical idea from becoming an unchallenged requirement.

For each capability, write five parts:

1. Actor and goal — who needs the behavior and why.
2. Normal flow — what happens when valid input is supplied.
3. Input and output rules — required fields, formats, returned data, and visible state.
4. Failure behavior — invalid input, missing permissions, duplicate requests, timeouts, and partial failure.
5. Acceptance evidence — test, inspection, interface example, log entry, or manual check that proves completion.

Avoid requirements such as “make the endpoint fast” unless the project has a verified measurement method and an approved threshold. A safer requirement is “the endpoint returns the documented response shape for valid and invalid payloads, and the integration test covers both paths.” If performance is genuinely part of the product decision, define the measurement environment and threshold separately.

A compact Specification example

Feature: Export filtered project records

Actor:
- An authenticated project administrator.

Behavior:
- The administrator can request an export for one selected project.
- The export includes only records matching the selected date range.
- The system returns a job identifier before file generation completes.

Validation:
- The project identifier must belong to the administrator's organization.
- The date range must contain a valid start and end date.
- Empty result sets produce a valid export with headers.

Failure behavior:
- Unauthorized users receive the standard authorization error.
- Invalid date ranges are rejected without creating a job.
- A failed background job records an error state visible to the administrator.

Acceptance evidence:
- Authorization tests cover an allowed and denied organization.
- Date validation tests cover missing, reversed, and valid ranges.
- The response contract is checked against the API schema.
- A manual review confirms that the generated file contains no unrelated records.

This is enough for an agent to reason about behavior without forcing a specific database schema or class structure. The Specification becomes executable when every important sentence maps to a test, static check, contract check, or human review.

Stage three: generate the design and task list

Do not ask the agent to jump from a feature paragraph directly to code. First require an implementation plan that identifies the affected modules, interfaces, dependencies, data flow, risks, and verification commands.

The plan should answer:

• Which existing components can be reused?
• Which files or packages are likely to change?
• Does the feature alter a public interface or stored data?
• What must be implemented before another component can be tested?
• Which decisions remain uncertain?
• What is explicitly outside the feature scope?

The official Spec Kit workflow reference shows a sequence that can place review gates between Specification, planning, task generation, and implementation. Those gates are important because an incorrect plan can produce internally consistent code that still solves the wrong problem.

Artifact	Primary question	Review gate
Project constraints	What must remain true across the repository?	Confirm rules are stable and enforceable
Specification	What behavior must exist?	Confirm scope, exceptions, and acceptance evidence
Plan	How will the repository provide that behavior?	Confirm dependencies, interfaces, and risks
Tasks	What can be implemented and verified independently?	Confirm order, file scope, and exit conditions
Diff and test report	What actually changed and what passed?	Confirm traceability to the original Specification

A task should be small enough to review without reconstructing the entire feature. A strong task includes an identifier, an exact outcome, file or module scope, dependencies, and a verification command.

T004: Add organization authorization check
Scope: src/auth/organization_access.ts and tests/auth/organization_access.test.ts
Depends on: T002
Acceptance: allowed organization passes; unrelated organization is rejected;
all authorization tests pass.

The generated task list should also distinguish parallel work from dependent work. Documentation and an isolated test fixture may proceed in parallel. A database migration cannot be treated as independent of the model and deployment checks that consume it. The official task-generation guidance emphasizes dependency ordering, file paths, user-story grouping, and checkbox-based task records.

Stage four: execute one task at a time

The safest implementation loop limits the agent’s context to the current task, the relevant Specification section, the related code, and the commands needed to validate the change.

A repeatable five-part loop is:

1. Load the boundary
   Provide the current task, its parent requirement, relevant project constraints, affected files, and explicit exclusions.

2. Request a plan before edits
   Require the agent to list intended files, logic changes, assumptions, and validation steps. Stop if the plan includes unrelated modules.

3. Apply the smallest coherent diff
   The agent should implement the current task without opportunistic refactoring, dependency replacement, formatting churn, or unrelated bug fixes.

4. Run the declared checks
   Execute the narrowest relevant test first, then the broader lint, type, integration, or build checks required by the project.

5. Record evidence and update status
   Mark the task complete only when the diff, command output, and remaining risks are recorded. A task that merely “looks implemented” is not complete.

This approach controls context drift. The agent does not need the entire product history for every edit. It needs the current decision, the constraints that cannot change, and enough surrounding code to preserve interfaces.

When the agent proposes a change outside the task scope, the correct response is not always another corrective prompt. First determine whether the Specification or plan omitted a dependency. If so, update the artifact and create a new task. If not, reject the expansion and keep the diff narrow.

Stage five: map acceptance back to the Specification

Acceptance should not begin with “does the code look good?” It should begin with a traceability table that maps every significant requirement to evidence.

Specification statement	Evidence	Status rule
Only authorized administrators can export	Authorization unit and integration tests	Both allowed and denied paths pass
Date range must be valid	Validation tests and API contract check	Missing, reversed, and valid inputs are covered
Empty results still produce headers	Export fixture and manual file inspection	Output is structurally valid
Failed jobs expose an error state	Background-job test and interface check	Failure is visible without exposing secrets

A complete review normally combines:

• Automated unit and integration tests.
• Static analysis, formatting, type checks, and build validation.
• API or schema contract checks.
• Manual inspection of user-visible behavior.
• Security review for permissions, secrets, data exposure, and unsafe defaults.
• Diff review against protected areas and the task’s stated file scope.

If one acceptance item fails, return to the corresponding layer:

• A behavior mismatch usually returns to the Specification.
• A dependency or interface problem returns to the plan.
• A missing implementation detail returns to the task.
• A regression returns to the code and test task.
• A prohibited change returns to the project constraints.

Continuing to add temporary prompts after a failed check hides the actual source of the problem. The workflow should make the failure location explicit.

Stage six: version the Specification with the code

A Specification that lives only in a chat transcript cannot reliably guide future maintenance. Store it beside the implementation artifacts in version control, using a stable feature identifier and a change history.

The minimum versioned set is:

.specify/
  memory/
    constitution.md

specs/
  001-export-project-records/
    spec.md
    plan.md
    tasks.md
    checklist.md

The exact directory names can vary by tool or repository. The principle is more important: the requirement, design decisions, tasks, checks, and implementation history must remain discoverable together.

When a requirement changes, use this order:

1. Edit the Specification and mark the changed behavior.
2. Describe the impact on interfaces, data, permissions, tests, and documentation.
3. Review whether the existing plan is still valid.
4. Regenerate or revise affected tasks.
5. Implement the change in a new diff.
6. Re-run acceptance against both the changed requirement and nearby unchanged requirements.

Do not overwrite the old intent without leaving a trace. A short change note can explain why a rule changed, which task absorbed the work, and which tests were added. This becomes especially important when several developers or agents work on the same feature over time.

The official Spec Kit upgrade guidance also illustrates why project artifacts should be treated as durable files. Workflow tooling may evolve, but implementation plans, task records, and project principles need to remain safe and reviewable during updates.

Reusable execution checklist

Use the following checklist before allowing an AI Coding Agent to modify a shared branch:

• [ ] Project technologies, package commands, and runtime assumptions are recorded.
• [ ] Protected directories, security boundaries, and forbidden changes are explicit.
• [ ] The feature has one bounded Specification with a clear scope.
• [ ] Normal behavior, invalid input, failure behavior, and permissions are defined.
• [ ] Each important requirement has a test, inspection, or other acceptance method.
• [ ] The agent has produced a design before broad implementation begins.
• [ ] The design identifies affected files, dependencies, interfaces, and risks.
• [ ] Tasks are dependency-ordered and small enough to review independently.
• [ ] Every task states its file scope and exit condition.
• [ ] The agent explains its plan before editing.
• [ ] Unrelated refactoring is excluded from the current diff.
• [ ] Narrow verification runs before repository-wide checks.
• [ ] Test output and unresolved risks are attached to the task record.
• [ ] Acceptance results map back to the original Specification.
• [ ] Requirement changes are committed before corresponding code changes.
• [ ] The environment can be reset and the workflow can be reproduced by another developer.

The checklist is not a substitute for engineering judgment. It is a control against silent omissions, especially when the generated code appears convincing.

FAQ

The workflow becomes easier to adopt when the first project uses a small feature with visible acceptance evidence, such as a form, API endpoint, background job, or command-line operation. For larger systems, the official Spec Kit concept guide for bounded specifications recommends dividing broad roadmaps into independently specified slices, so each slice can complete its own specification, plan, task, and implementation cycle.

For teams that need resumable automation, the official workflow documentation describes persisted steps, review gates, status checks, and resume behavior. That model is useful when a developer must pause after Specification review or when an implementation environment needs to be reset before continuing.

Current environment versus a rented Mac development environment

A local workstation can be the best choice for long-running, stable workloads when the team already owns the required hardware, has reliable test dependencies, and needs direct access to physical devices or private network equipment. It becomes less convenient when the current setup is shared, difficult to reset, tied to one developer’s machine, or missing a reproducible Apple toolchain.

The recurring weaknesses are usually concrete: local environments drift from the documented setup, shared machines create queueing and permission problems, and rebuilding a clean test state can consume more time than the feature itself. A rented Mac environment from Zutcloud can be the better fit for temporary AI Coding Agent work, isolated validation, remote access, or a short-lived Apple-based build and test cycle. The appropriate next step is to review the available Mac rental options only after checking the required tools, reset procedure, test dependencies, and access policy.

For setup questions, the Zutcloud Help Center can help verify whether the intended workflow fits the available remote environment. The key decision is not whether remote hardware is universally better; it is whether a clean, resettable Mac environment removes a current bottleneck without creating a new one around long-term cost, physical access, or persistent high-load work.

FAQ

How should a developer begin with Spec-Driven Development?

Start by writing the project constitution: supported technologies, directory rules, security boundaries, protected files, testing commands, and the definition of done. Then describe one bounded feature in plain language and ask the AI Coding Agent to produce a Specification. Review that artifact before allowing it to create a plan or modify code.

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

A Specification is ready when another developer can determine whether each requirement is satisfied without guessing the intended behavior. It should define user-visible behavior, inputs, outputs, validation rules, failure cases, permissions, data changes, and acceptance evidence. It does not need to prescribe every function name or implementation detail.

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

The agent should first map the Specification to architecture, dependencies, affected files, interfaces, and verification commands. It can then create dependency-ordered tasks grouped by user story or capability. Each task should have one clear outcome, an explicit file scope, a prerequisite list, and a validation method so it can be implemented and reviewed independently.

How do teams prevent code from getting out of control after a Specification changes?

Treat the Specification as a versioned engineering artifact, not a disposable prompt. When a requirement changes, update the Specification first, record the affected design and tests, regenerate or revise the task list, and review the resulting diff. Do not let the agent patch symptoms until the new requirement and its impact are recorded.

Further reading

• A controlled multi-agent coding workflow with task contracts, isolated worktrees, testing, review, and merge gates
• Designing secure agent workspaces with layered storage, policy gateways, snapshots, and audit controls
• A practical guide to parallel AI coding, environment validation, task isolation, and acceptance testing