What is spec coding?
Spec coding does not require a long PRD. For most engineering tickets, the useful version is compact: desired behavior, non-goals, acceptance criteria, edge cases, test expectations, affected systems, and review rules. The point is to make "done" visible before anyone, human or agent, starts changing files.
This matters more with AI coding agents because agents are fast at producing code but not automatically aligned with your product intent, repo conventions, security boundaries, or merge policy. A spec gives the agent a smaller target and gives the reviewer an objective checklist.
If you are starting from a ticket, the AI agent ticket template is a practical way to convert rough intent into an agent-ready spec.
Spec-to-code AI workflow
A spec-to-code AI workflow starts with a requirement and ends with a reviewable pull request. The agent should not be treated as a magic implementation box. It should be given a scoped spec, asked to produce a plan, tested against known criteria, and kept inside normal engineering controls.
Start with the user story, bug report, screenshot, support request, or backlog ticket.
Clarify behavior, constraints, data states, permissions, success criteria, and explicit non-goals.
Have the coding agent identify likely files, test strategy, migration risks, and review-sensitive areas before editing.
Prefer failing or updated tests that describe the expected behavior before implementation code is accepted.
Keep output inside branch, CI, code review, and merge policy rather than treating generated code as finished.
Specship is in private beta, so the product language is intentionally careful: it is designed to help teams move from ticket to pull request, not to guarantee unattended production automation. See the ticket-to-PR AI agent workflow for the product use case.
Spec-driven development with coding agents
Spec-driven development with coding agents works best when the work is bounded enough to describe and verify: feature tickets, bug fixes, UI changes, tests, refactors, migrations, integration chores, and support-driven product improvements. It is weaker for open-ended architecture bets, unclear product direction, sensitive production operations, or any task where the team cannot describe what done means.
What the spec agent should clarify
- Who or what system behavior changes
- Which states, permissions, and failure paths matter
- What code areas are likely in scope
- What is explicitly out of scope
What the coding agent should produce
- An implementation plan tied to the spec
- Tests or checks that reflect acceptance criteria
- A pull request with review notes
- A clear record of what was verified
You can review concrete patterns on the examples page, and teams evaluating deployment controls should also read the security overview. Specship does not claim completed SOC 2 certification on this page.
Spec coding vs prompt-first AI coding
Prompt-first AI coding starts with an instruction and relies on the model to infer missing requirements. Spec coding starts by making those requirements explicit. The difference is not whether you use AI. The difference is whether the AI is guided by a reviewed software spec or by a one-off prompt.
How to write an agent-ready software spec
An agent-ready software spec should be direct enough for a human teammate and strict enough for an AI coding agent. Keep it short, but make every acceptance criterion observable.
- State the user or system behavior that should change.
- List what should remain unchanged so the agent does not broaden scope.
- Name likely files, components, endpoints, jobs, or packages if you know them.
- Define acceptance criteria as reviewable outcomes, not vague preferences.
- Call out edge cases, permissions, empty states, error states, and migrations.
- Tell the agent which test runner, fixture, or existing pattern to use.
- Mark protected paths or security-sensitive changes for human review.
For early teams, this can live in a ticket template. For more mature teams, the same structure can connect to branch creation, CI checks, and pull request review. Specship pricing is not final while the product is in private beta; join the waitlist for access updates.
Visual: agent-ready spec to pull request
Bug report, feature idea, support case, or backlog item.
Acceptance criteria, constraints, tests, risks, and review gates.
Implementation stays scoped to the approved spec.
CI, reviewer notes, and merge control remain in the team workflow.
Spec coding FAQ
What is spec coding?
Spec coding is the practice of writing a clear software specification before implementation. For AI coding agents, the spec usually includes acceptance criteria, constraints, edge cases, test expectations, and review rules.
How does spec-to-code AI work?
A spec-to-code AI workflow turns a ticket or requirement into an agent-ready spec, asks a coding agent to plan and test against that spec, then keeps the resulting changes inside branch, pull request, CI, and human review controls.
What is a spec agent?
A spec agent is an AI assistant or workflow that helps transform rough requirements into implementation-ready specs for coding agents. It should clarify ambiguity and preserve human review before code is merged.
Is spec-driven development with coding agents fully automated?
Not reliably. Spec-driven development with coding agents can reduce manual handoff work, but teams should still use tests, CI, code review, security review, and scoped permissions for production software.
How do I write an agent-ready software spec?
Start with the desired behavior, define what should remain unchanged, list acceptance criteria, name likely files or systems in scope, specify tests, add edge cases, and state when human review is required.