From product thinking to agent implementation
AI coding agents can build features in minutes. But they build the wrong features without product context. Delvyn Studio generates context-rich specifications that AI agents implement correctly — from your vision, strategy, discovery, and OKRs.
The Agent Specification Workflow
From product idea to AI agent implementation in 7 steps. Every specification includes your product vision, strategic guardrails, and acceptance criteria.
Select idea or key result
Pick a validated idea or OKR key result to spec out
Context assembly
Vision, strategy, guardrails, and discovery are assembled automatically
AI generates spec
Structured, agent-ready specification is generated
AI coach reviews
Specification Coach checks for completeness and clarity
Push to GitHub / Linear
One-click push creates an issue with full context
Agent implements
Copilot or Claude Code implements the spec as a PR
Capture learnings
Rate and improve for better future specs
Seven capabilities that connect strategy to AI execution
Each pillar feeds context into the next — so every agent spec reflects your product direction
Agent Specification Engine
The specification IS the implementation instruction
Generate context-rich specifications designed for AI coding agents — not traditional PRDs. Select an idea, discovery, or OKR key result and Delvyn Studio assembles your product vision, strategy, guardrails, and acceptance criteria into a structured spec that agents implement correctly.
- One-click spec generation from ideas, discoveries, or key results
- Automatic context assembly from vision, strategy, and OKRs
- AI Specification Coach reviews quality before push
- Push directly to GitHub Issues or Linear as agent tasks
- Spec quality scoring and improvement tracking
Product Vision & Strategy
Strategic guardrails for AI agent alignment
Define product vision using the Geoffrey Moore framework. Set strategic guardrails, anti-goals, and architectural boundaries that export as AI agent context — ensuring every agent implementation stays aligned with your product direction.
- Geoffrey Moore vision framework with AI-assisted generation
- Strategic guardrails that export to agent instructions
- Anti-goals and architectural boundaries for AI alignment
- Context sync to repository instruction files
Product Discovery & Ideas
Validate before you build — the 4-risk model
Validate ideas through structured discovery with the 4-risk model (value, usability, feasibility, viability). AI-powered feasibility assessment helps determine if an idea is agent-implementable or requires human engineering.
- 4-risk discovery model (value, usability, feasibility, viability)
- AI feasibility assessment for agent implementation
- Structured discovery project management
- Direct path from validated idea to agent specification
OKR Framework
Key results with agent-ready acceptance criteria
Set OKRs with measurable key results that include structured acceptance criteria — ready to be converted into agent specifications. Plan, check in, and evaluate with AI-powered insights and progress tracking.
- Key results with structured acceptance criteria
- Generate agent specs from any key result
- AI-powered OKR suggestions and progress insights
- Check-in workflows and cycle evaluation
Learning Capture & Spec Quality
Every implementation makes the next spec better
After an AI agent implements a feature, capture what worked and what didn't. Rate specification quality, note improvements, and feed learnings back into future spec generation. No competitor has this.
- Post-implementation learning capture (2-minute workflow)
- Specification quality ratings (1-5 scale)
- AI-assisted learning pattern detection
- Compounding spec quality improvement over time
Integrations
Push specs to where AI agents work
Delvyn Studio delivers value through integrations, not just its own UI. Push agent specifications directly to GitHub (for Copilot), Linear (for Claude Code, Devin), and Jira. Connect your IDE to your product context with the MCP Server.
- GitHub — push specs as issues for Copilot coding agent
- Linear — create agent tasks for Claude Code, GitHub Copilot, Devin
- Jira integration for enterprise teams
- MCP Server for real-time IDE context (Professional+)
- Context sync to repository instruction files
AI-Powered Everything
AI built for product thinking, not documents
AI assistance across every step of the product thinking process — from generating vision statements to coaching specification quality. Purpose-built for product management, not generic document generation.
- AI vision and strategy generation
- AI OKR suggestions and key result writing
- AI discovery insights and risk assessment
- AI Specification Coach for quality review
- AI-assisted learning pattern detection
Built for AI-native product teams
Capabilities that no other product management tool provides
AI Specification Coach
Reviews specs for completeness, clarity, and strategic alignment before you push to agents.
Learning Flywheel
Capture post-implementation learnings that compound specification quality over time.
Context Sync to Repository
Auto-sync product vision and strategy to .github/instructions/ files for coding agents.
Strategic Guardrails
Define anti-goals and boundaries that export as AI agent constraints.
Enterprise Security
SSO, SCIM, encrypted data at rest, role-based access, and audit logging.
MCP Server
Let AI coding agents query your product context in real-time from VS Code, Cursor, or any MCP-compatible IDE.
Frequently Asked Questions
How Delvyn Studio differs from Linear, Productboard, and traditional PM tools
What makes Delvyn Studio different from other product management tools?
Delvyn Studio is the only platform that generates agent-ready specifications from structured product thinking. Unlike traditional tools, we bridge the gap between product strategy and AI-powered engineering:
- Agent Specification Engine: Generate structured specs that AI coding agents (Copilot, Claude Code) implement directly
- Product context delivery: Vision, strategy, guardrails, and OKRs flow into every specification automatically
- Learning capture: Post-implementation learnings compound spec quality over time — no competitor has this
How is Delvyn Studio different from Linear?
Linear is a powerful execution layer with AI agents, initiatives, and documents. Delvyn Studio sits upstream — owning the product thinking that makes those capabilities effective.
Linear (Execution Layer):
- Issue tracking and sprint management
- AI agent delegation (Claude Code, Copilot)
- Project planning and initiatives
- Documents and PRDs (ChatPRD)
Delvyn Studio (Thinking Layer):
- Product vision, strategy, and guardrails
- Structured discovery and validation
- OKR framework with acceptance criteria
- Agent spec generation with context
- Learning capture and spec quality tracking
Use both: think in Delvyn Studio, execute in Linear. Specs push directly to Linear as agent tasks.
How is Delvyn Studio different from Productboard Spark?
Productboard Spark generates traditional documents (PRDs, briefs). Delvyn Studio generates agent-ready specifications.
- Specs, not docs: Our output pushes directly to GitHub/Linear as agent tasks — not PDF documents
- Full product context: We hold vision + strategy + discovery + OKRs together. Spark has none of this.
- Learning capture: Post-implementation learnings improve future specs. Spark has no learning loop.
- Free engineers: Only Admins/ProductLeaders pay $9/month. Productboard charges $25-80 per maker.
What integrations are available?
Delvyn Studio delivers value through integrations — specs flow to where AI agents work:
- GitHub: Push specs as issues, assign to Copilot coding agent
- Linear: Create agent tasks for Claude Code, GitHub Copilot, and Devin
- Jira: Push specs as Jira issues for enterprise teams
- MCP Server: Real-time product context in your IDE (Enterprise)