AI-Assisted Development
Specs-Driven Development with AI
We turn PRDs, user stories, wireframes, workflow notes, and technical specs into agent-ready implementation plans, then build with AI-assisted engineering under human review.
Why it matters
Use specifications to keep AI coding agents aligned, reviewable, and production ready.
Specs-driven development is how we make AI-assisted software delivery controllable. The specification is not documentation after the fact. It becomes the instruction layer for AI coding agents and engineers: product behavior, user roles, edge cases, data rules, acceptance criteria, review checkpoints, and handover notes.
That gives AI room to move quickly without inventing the product as it goes. Agents generate code against explicit behavior, engineers review against the same contract, and changes can be traced back to the source requirement instead of disappearing into prompts, chat history, and silent assumptions.
Primary intent
Use a written specification as the control layer for AI coding agents, implementation, review, testing, and handover.
Fit
Where this work creates leverage.
Ideal for
- Product teams using PRDs, wireframes, or detailed user stories
- Founders who want AI coding speed without product drift
- Organizations with compliance, audit, or approval requirements
- Teams that need agent-generated code aligned to documented behavior
Typical use cases
- Turning a PRD into a SaaS feature
- Building workflow software from process documentation
- Implementing a portal from Figma and user stories
- Extending a product with documented acceptance criteria
Scope
What Solutyics delivers.
Deliverables
- Agent-ready specification and implementation plan
- Acceptance criteria mapped to features, screens, APIs, and data behavior
- Production application or feature set built with AI-assisted engineering
- Human review notes, tests, and traceability back to the spec
- Handover documentation for future agent-assisted changes
Considerations
- AI agents need explicit behavior, constraints, and acceptance criteria
- Ambiguous requirements should be resolved before code generation accelerates them
- Specs should be updated when product decisions change
- Human review remains necessary for architecture, security, and maintainability
Outcomes
What should improve after the work.
Faster AI-assisted delivery with clearer boundaries
Less product drift between requirements and generated code
Cleaner review, acceptance, and stakeholder sign-off
A codebase aligned to documented product intent
Process
How the work moves
Prepare the spec for agents
We review requirements, edge cases, roles, acceptance criteria, constraints, and missing decisions before implementation starts.
Plan the build path
We translate the spec into architecture, data models, APIs, screens, release slices, and prompts or tasks that agents can execute safely.
Build with AI under review
We implement in controlled increments, using AI where it improves speed while reviewing each piece against the written behavior.
Keep the spec alive
We tie tests, handover notes, and implementation decisions back to the spec so future AI-assisted changes have a reliable source of truth.
FAQ
Questions that shape the work.
Is this just writing specs before development? +
No. The spec is actively used to guide AI coding agents, implementation tasks, review, tests, and handover. It is the operating contract for the build, not a document that gets ignored once coding starts.
Do we need a perfect specification before starting? +
No, but the important behavior should be explicit. If the spec has gaps, we identify and resolve them before AI-assisted development turns those gaps into hidden implementation assumptions.
Can you work from Figma designs and PRDs? +
Yes. Figma, PRDs, user stories, spreadsheets, workflow notes, and technical docs can all become the source material for an agent-ready implementation plan.
How do AI coding agents fit in? +
Agents help produce code faster once the expected behavior, constraints, data model, and acceptance criteria are clear. Engineers still review architecture, security, maintainability, and whether the result matches the spec.
What happens if requirements change mid-project? +
We update the spec, assess the impact, and adjust the implementation plan. The spec remains the source of truth so future AI-assisted changes do not depend on old prompts or memory.
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Next
Use specs to make AI-assisted development controllable.
Bring the PRD, wireframes, user stories, or rough notes. We will turn them into an agent-ready plan and build from it.