Educational Guide
What are Agent Specifications?
The missing layer between product thinking and AI code generation. Learn what agent specs are, why they matter, and how they transform the way AI-native teams build software.
The problem: AI agents lack context
AI coding agents like GitHub Copilot, Claude Code, Cursor, and Devin are transforming software development. But there's a critical gap: these agents are only as good as the context they receive.
What happens without agent specs
- •Vague prompts like "build a settings page" produce generic, off-strategy features
- •AI agents don't know your product vision, strategic boundaries, or success criteria
- •Early adopter teams report spending hours per feature reworking AI-generated code that missed the point
- •No learning loop — the same context gaps repeat every sprint
Agent specifications: the solution
An agent specification is a structured, context-rich document designed specifically for AI coding agents. It includes everything an agent needs to implement a feature correctly on the first try:
Product Vision & Strategy
The strategic direction and guardrails that define acceptable solutions
Discovery Insights
Validated customer needs, risks assessed, and assumptions tested
OKR Success Criteria
Measurable outcomes the feature must drive, connected to team objectives
Implementation Context
Technical constraints, architecture patterns, API contracts, and coding standards
Acceptance Criteria
Precise, testable scenarios with edge cases and error handling
Learnings from Past Specs
What worked and what didn’t in previous implementations
Agent specs vs PRDs vs user stories
How agent specs work in practice
Agent specifications are generated from your existing product thinking \u2014 you don't write them from scratch.
Think
Define vision, validate strategy, run discovery, set OKRs
Generate
Delvyn Studio assembles all context into a structured agent spec
Push
Push to GitHub (Copilot), Linear (Claude Code), or sync to repo
Learn
Capture implementation learnings → future specs improve
Who needs agent specifications?
- Product Leaders: Ensure AI agents build what you envisioned, not what they guessed
- Engineering Managers: Reduce rework from vague requirements. Get it right the first time.
- Teams using GitHub Copilot: Push context-rich issues that Copilot Agent can implement with strategic alignment
- Teams using Claude Code / Devin: Create Linear tasks with full product context for AI agent delegation
- Teams adopting Cursor: Sync specification context directly to repository instruction files
Generate your first agent specification
Start free with 5 agent spec generations. See the difference between a vague prompt and a context-rich specification.