Platform Workflow

How Feature1 Works

From product idea to deployed code — powered by domain intelligence and AI agents

The Feature1 Pipeline

Six stages from repo connection to shipped code

01

Connect Your Repository

Onboard your GitHub, GitLab, or Bitbucket repository in minutes. Feature1 securely indexes your codebase and establishes a persistent connection to track changes over time.

GitHubGitLabBitbucketOAuth 2.0
02

Build Domain Intelligence

The AI constructs a semantic knowledge graph of your codebase — understanding modules, dependencies, conventions, and architectural patterns. This graph powers every downstream decision.

Knowledge GraphSemantic AnalysisAST ParsingDependency Mapping
03

Plan Features

Submit a feature idea in plain language. Feature1 runs a 6-stage AI analysis pipeline — scoping, risk assessment, dependency analysis, effort estimation, acceptance criteria drafting, and implementation planning.

6-Stage PipelineRisk AnalysisEffort EstimationScope Definition
04

Generate User Stories & ACs

User stories and acceptance criteria are auto-generated from feature analysis, fully aligned with your domain model. Each story is traceable, testable, and ready for implementation.

User StoriesAcceptance CriteriaBDD FormatTraceability
05

Implement with AI

Choose Autopilot for fully autonomous implementation or Copilot to review and approve each step. The AI writes code consistent with your existing patterns, style, and architecture.

AutopilotCopilotContext-AwareStyle Consistent
06

Ship

A pull request is created automatically with a full diff, inline comments explaining decisions, and auto-generated release notes. Merge when ready — Feature1 handles the rest.

Auto PRRelease NotesInline DocsCI/CD Ready

Choose Your Mode

Two ways to work with Feature1 — pick the level of control that suits your team

Autopilot

Fully Autonomous

Feature1 takes the wheel. The AI agent plans, generates user stories, writes acceptance criteria, implements code, creates PRs, and generates release notes — end to end, without human intervention.

  • Zero manual steps — AI handles the full lifecycle
  • Feature analysis to PR without interruption
  • Auto-generated release notes on every merge
  • Continuous learning from merged code
  • Ideal for well-scoped, repeatable work
Best for: Teams who want maximum velocity with minimal overhead
Copilot

Human-in-the-Loop

You stay in control at every stage. Feature1 proposes, you approve. Review feature plans, user stories, acceptance criteria, and generated code before anything moves forward.

  • Approve or edit each stage before proceeding
  • Review user stories and ACs before implementation
  • Inspect generated code before PR creation
  • Override AI decisions at any checkpoint
  • Full audit trail of every AI action
Best for: Teams who want AI leverage while keeping architectural ownership

MCP Integration

Connect Claude Code and other AI agents directly to Feature1 via the Model Context Protocol

What is MCP?

The Model Context Protocol (MCP) is an open standard that lets AI agents securely connect to external tools and data sources. Feature1 exposes its entire workflow through an MCP server — meaning any compatible agent can plan, implement, and ship features programmatically.

Claude Code
Cursor
Windsurf
Custom Agents
feature1-mcp-server
create_feature()// submit a new feature idea
get_planning_context()// fetch codebase intelligence
list_user_stories()// retrieve generated stories
implement_ac()// trigger AI code generation
mark_ready_for_testing()// flag AC for QA
create_pr()// open pull request with release notes
Secure by Default
OAuth-scoped tokens, no plain-text secrets
Real-Time Context
Always-fresh codebase knowledge graph
Bidirectional
Read and write across the full workflow
Agent Agnostic
Works with any MCP-compatible client
Workflow State
Track progress across every pipeline stage
REST Fallback
Standard API available alongside MCP

Ready to ship faster?

Connect your repo, let Feature1 build domain intelligence, and start delivering features at AI speed.