Scale. Measure. Optimize.
The loop that lets one developer keep a funded engineering team’s pace — in afternoons, not quarters.

Your Engineering Workforce, formed by AI Agents, built on Claude and Codex — worktree-isolated agents to scale, a five-component score and cost ledger to measure, and per-stage routing to optimize on the evidence.

See How It WorksSee the Loop
Per-task git worktree isolationEvery model call cost-tagged by sub-stagePowered by Claude & Codex

AI Agent Board

20 isolated worktrees (start at two)

To Do

AI

Code Review

Agent-001

In Progress

AI

Build Feature

Agent-002

65%
AI

Fix Bug

Agent-003

Done

AI

Deploy

Agent-004

Completed

The Gap Between Planning and AI Execution

Traditional Tools

Linear, Jira, Notion

No AI agent execution
Manual task handoff
Context switching overhead
Limited automation

AI Assistants

Claude Code, Cursor, Gemini CLI, GitHub Copilot

No project structure
One-off responses
No task tracking
No collaboration
No parallel execution

CrewDeck bridges the gap

The only platform that combines project management structure with deep orchestration across Claude Code and Codex — manage all agents from one board

Scale. Measure. Optimize.

The three-step loop that lets one developer run a funded engineering team’s discipline on an afternoon’s cycle. The order is the argument: you cannot optimize what you haven’t measured, and there is nothing worth measuring until you’ve scaled past what fits in your head.

  1. Scale

    Your ceiling moves from how fast you can type to how many isolated units of work you can keep in flight.

    • Every agent session runs in its own git worktree — a separate working directory on a separate branch, sharing the same underlying repository data, so one agent cannot corrupt another’s uncommitted work.
    • 20 concurrent sessions is the architectural ceiling, not the shipping default. Concurrency is a dial you raise as your process earns it — the recommended start is two agents, not twenty.
    • Work moves through five Kanban stages — To Do, In Progress, In Review, Document, Done — and each stage fires its own command sequence, so nothing reaches Done without transiting a fixed pipeline.

  2. Measure

    Outcome metrics, not input metrics, attached to three units you can actually change: the agent, the spend, and the decision.

    • A five-component agent score — success rate, satisfaction, efficiency, recency, and override decay — multiplied by a confidence factor that doesn’t reach 100% until an agent has 10 recorded runs.
    • Every model call is cost-tagged by the sub-stage that fired it, roughly 26 of them, rolled up into cost per merged line of code and a rework-share number that flags specification problems before they look like model problems.
    • Duels run one task twice under different configurations and score the resulting diffs with a blind AI judge — no agent names, no model names — feeding per-agent Elo ratings with each arm’s cost attached.

  3. Optimize

    With the evidence in hand, the moves become concrete — and the most obvious one ships switched off on purpose.

    • A one-click, per-project, reversible action down-tiers the two planning sub-stages to a smaller model — off by default, because a weaker plan can raise downstream execution cost by more than it saved.
    • A cache boundary bills run-invariant context (your conventions file, cross-repository docs) as a cached prefix, and a shared ledger at the repository root gives every agent a warm-start read instead of rediscovering the same facts twenty times.
    • Multi-agent debates have specialized agents propose, critique, and synthesize a consensus, written to a markdown decision record and committed to the repository.

A funded engineering team runs the same loop — but their cycle time is measured in quarters, and yours is measured in afternoons. CrewDeck’s own repository has merged more than 2,300 pull requests since launch, gated by CI coverage thresholds of 95% statements, 95% lines, 93% branches, and 90% functions. Fifty engineers still buy things this loop doesn’t replace: enterprise sales, support at scale, deep domain knowledge, compliance depth, and the ability to survive one person getting the flu. Velocity is not direction.

Read the write-upListen to the episodeWatch the video
Business Impact

Transform How Your Team Ships

See the measurable impact of AI-powered development workflows on your engineering team

01

Developer Velocity

0

2 agents to start — scale to a 20-worktree ceiling

Before

Manual workflows slow you down

  • Constant context-switching between tasks
  • Manual PR creation and reviews
  • Time lost on repetitive boilerplate
After

AI handles the routine

  • Each agent runs in its own git worktree — separate directory, separate branch, shared repository data
  • Developers focus on architecture decisions
  • Automated PR workflows save hours daily
  • Choose the best AI tool for each task — no vendor lock-in
  • Agent performance analytics ensure the right specialist for every task
02

Parallel Development

0

Ship 20 features in parallel

Before

Sequential bottlenecks

  • One feature at a time per developer
  • Blocked pipelines waiting for reviews
  • Merge conflicts delay releases
After

True parallel execution

  • Up to 20 AI agents working simultaneously — 20 is the architectural ceiling, not the default; start at two and raise the dial as your process earns it
  • Isolated git worktrees prevent conflicts
  • Ship an entire sprint in a single day
  • Chain commands for multi-step automated workflows
03

Code Quality

0

5 stages, review and improve as separate passes

Before

Quality inconsistencies

  • Code review backlog growing daily
  • Inconsistent coding standards
  • Bugs discovered late in production
After

Consistent excellence

  • Review and improve run as two separate passes — the reviewer is forbidden from implementing its own fixes, so findings have to survive being written down
  • Automated testing and validation
  • Consistent patterns across codebase
  • Data-driven agent selection improves first-pass success rates
04

Cost Efficiency

0%

Scale without scaling headcount

Before

Expensive scaling

  • High hiring costs for each new project
  • Months of onboarding time
  • Limited capacity constrains growth
After

Efficient scaling

  • AI agents augment team capacity instantly
  • No recruiting or onboarding delays
  • Cost per merged line of code, not tokens burned, measures the result
  • Mix and match AI providers to optimize cost vs. capability
2
20
5

Join teams shipping faster with AI-powered workflows

Everything You Need to Ship with AI

A complete platform for managing AI-powered development workflows

Project Management

Organize your teams with visual Kanban boards. Create projects for frontend, backend, infrastructure — each with dedicated AI agents.

Powered by Claude Code and Codex

Deep integration with Claude Code and Codex for maximum agent performance. Purpose-built orchestration that gets the most out of every agent session.

Command Chaining

Define multi-step automated workflows where agent outputs feed into the next task. Build, test, and deploy — triggered from a single command.

Git Worktree Isolation

Run up to 20 tasks in isolated worktrees simultaneously — 20 is the architectural ceiling, not the default; start at two. No merge conflicts, no context pollution.

Live Task Execution

Up to 20 concurrent terminal sessions with real-time output streaming. Watch all your AI agents work with full visibility.

Automated PR Workflow

From task to production. Automated PR creation, semantic versioning, and streamlined review process.

Seamless GitHub Integration

Connect repositories instantly. Auto-clone, secure credential management, and direct commit access for AI agents.

29 Specialized Agents

Deploy from 5 categories: Orchestrators, Development, Quality & Security, Documentation, and Specialized. Build custom agents from your codebase in minutes.

Agent Performance Analytics

Track success rates, execution times, and quality scores. Data-driven recommendations help you assign the best agent for every task type.

Sequential, Swarm & Team Modes

Choose how agents collaborate. Sequential for predictable pipelines, Swarm for parallel orchestration, Team mode for coordinated multi-agent collaboration.

Task Intelligence

Create tasks in plain English. AI extracts structured fields, auto-decomposes epics into subtasks, validates dependency DAGs, and learns from past executions.

Security Pipeline

Three-layer security for AI agent execution. Command risk scoring, user-controlled approval gates, and capability-based agent permissions — enterprise-grade safety built in.

Ship Features in 5 Simple Steps

From idea to production with AI assistance

Click any step to learn more

01

Create a Project

Set up your project workspace and connect your GitHub repository

02

Discover Agents

AI automatically scans your codebase to discover and configure specialized agents with your preferred AI tools

03

Create Tasks

Add tasks to your Kanban board, assign agents, and chain commands for multi-step workflows

04

Watch Execution

Claude Code and Codex agents work in isolated worktrees with live terminal output and real-time visibility

05

Review & Merge

Review the PR, approve changes, and ship to production

AI Product Owner

From Roadmap to Tasks in Seconds

Upload your product roadmap. AI analyzes it, assigns the best AI tools per task, and generates 10 actionable development tasks ready for your Kanban board.

Product Roadmap

roadmap-q1-2026.md

Q1 2026 Product Roadmap
 
## User Authentication
- OAuth2 integration
- Session management
- Role-based access
 
## Analytics Dashboard
- Real-time metrics
- Custom reports
 
## Notifications
- Email alerts
- In-app messages

AI Product Owner

Ready to analyze

Generated Tasks

Ready for Kanban board

#01Implement user authentication flowhigh
#02Create dashboard analytics APIhigh
#03Design notification system schemamedium
#04Build settings preferences UImedium
#05Add rate limiting middlewarehigh
#06Create email template systemmedium
#07Implement search indexinglow
#08Add error tracking integrationmedium
#09Refactor database querieslow
#10Write API documentationlow
Tasks will appear here

No more manual task creation. Let AI understand your vision and break it down for you.

Task Intelligence

From Idea to Execution — AI Handles the Rest

Describe your feature in plain English. CrewDeck extracts tasks, creates dependencies, decomposes epics, and applies learnings from past executions.

01

Natural Language Input

Describe work in plain English. AI extracts structured fields — title, description, acceptance criteria, agent suggestions, and priority — all with confidence scores.

5,000char NL input
02

Auto-Task Decomposition

Break complex tasks into subtasks using Claude AI with automatic dependency wiring. One epic becomes a fully planned sprint.

20subtasks per decomposition
03

Epics

Group related tasks by theme or feature area with Kanban filtering and AI-assisted epic creation. Organize work at scale.

AIassisted grouping
04

Task Dependencies

DAG-validated dependencies with 3 relationship types — blocks, requires, related — and automatic cycle detection.

3dependency types
05

Cross-Task Learning

AI learns from past executions to improve future agent prompts. Every completed task makes the next one better.

Autoprompt improvement
5
AI capabilities
20
subtasks per decomposition
3
dependency types
DAG
cycle detection
Cross-Repo Intelligence

Give Your AI Agents the Full Picture

Automatically inject documentation and context from related repositories into every task execution.

Source Repositories

api-service

PROJECTS.md

Project config

shared-libs

architecture.md

Architecture docs

platform

RELEASE_NOTES.md

Release notes

Target Worktree

.crewdeck-context/
PROJECTS.md
architecture.md
RELEASE_NOTES.md
Agent Status

Waiting for context...

Cross-repository documentation injection

Automatic context file discovery and copying

Smart prompt enhancement with context references

Security: path traversal protection, size limits (1MB/file, 50MB total)

Context is everything. Give your agents the knowledge they need to ship better code.

AI Agent Orchestration

Watch Your Feature Come to Life

One request. Three execution modes — Sequential, Swarm, or Team. Specialized agents powered by Claude Code and Codex orchestrate your feature. Ship in minutes, not weeks.

Feature Request

“Add user authentication with OAuth2”

Feature Orchestrator

Coordinator

Systems Architect

Design

Database Designer

Data Layer

Backend Developer

API

Frontend Developer

UI

TypeScript Developer

Types

Security Auditor

Security

Coverage Orchestrator

Testing

Code Reviewer

Quality

Code Documenter

Docs

No human engineers were blocked during this simulation. Your team focuses on what matters.

Multi-Agent Consensus

When AI Agents Disagree, You Get Better Answers

Single-agent decisions have blind spots. Our multi-agent debate system — powered by Claude Code and Codex — assigns Advocate, Challenger, and Synthesizer roles to surface trade-offs you would miss.

The Problem with Single-Agent Decisions

A single AI agent optimizes for one perspective. When choosing between microservices and monoliths, it might favor architectural elegance over operational reality. When reviewing code, it might miss security implications that a specialized auditor would catch. Critical technical decisions deserve multiple expert viewpoints.

See It In Action

Architecture Decision Records

Your team is deciding between microservices and a modular monolith for a new e-commerce platform. Instead of endless meetings, let AI agents debate the trade-offs.

Systems Architect

Advocate

Backend Developer

Challenger

DevOps Engineer

Synthesizer

Systems Architect

Microservices enable independent deployment and scaling. Each team can own their domain with clear service boundaries.

Backend Developer

With our current team size of 8, microservices adds operational complexity that outweighs the benefits. Network latency and distributed debugging will slow us down.

DevOps Engineer

Start with a modular monolith using clear domain boundaries. Extract services only when team size or scale demands it. This gives us microservices-ready architecture without the overhead.

Debate will begin shortly...

Measurable Impact on Decision Quality

85%

reduction in architectural decision reversals

3x

faster consensus on code review disputes

40%

improvement in decision confidence scores

Zero

single-point-of-failure decisions

Why Multi-Agent Consensus Beats Single-Agent Tools

Traditional AI assistants give you one perspective. CrewDeck assigns specialized roles that challenge assumptions, surface edge cases, and synthesize balanced recommendations.

Single Agent

  • One perspective, potential blind spots
  • Optimizes for single criteria
  • No built-in challenge mechanism
  • Binary recommendations

Multi-Agent Debate

  • Three specialized viewpoints
  • Trade-off analysis built in
  • Systematic challenge and synthesis
  • Nuanced, documented decisions

Disagreement is a feature, not a bug. Better decisions emerge from structured debate.

Agent Intelligence

Measure: A Score That Can’t Be Gamed

Five weighted components — success rate, satisfaction, efficiency, recency, and override decay — held down by a confidence gate until an agent earns 10 runs.

Agent Performance

  1. Feature Orchestrator
    Success Rate94%
  2. Backend Developer
    Success Rate91%
  3. Code Reviewer
    Success Rate88%
  4. TypeScript Developer
    Success Rate86%
  5. Security Auditor
    Success Rate82%

Stop guessing which agent to use. Let performance data decide.

Enterprise Security

Three Layers of Protection for Every Command

Every command your AI agents execute passes through a multi-layer security pipeline — from OWASP-based risk scoring to capability-based agent permissions.

Command enters pipeline
LAYER 1

Command Risk Classification

OWASP-Based Scoring Engine

Every command is scored 0-100 using 30+ OWASP-aligned patterns. Safe commands pass through in sub-1ms. Risky commands are flagged before execution.

30+ risk patterns0-100 risk scoreSub-1ms classificationOWASP-aligned
LAYER 2

User-Controlled Approval

Interactive Approval Gate

Medium and destructive-risk commands require your explicit approval. Choose deny, approve once, or approve always — with standing approvals for trusted commands.

Deny / Approve Once / AlwaysStanding approvalsPer-command controlAudit logged
LAYER 3

Capability-Based Permissions

Android-Style Runtime Consent

Each agent declares required capabilities. 7 permission types — filesystem read/write, network, git read/write, shell execute/install — with explicit consent UI per agent.

7 capability typesPer-agent permissionsRuntime consent UIGranular control
Command approved — safe to execute
30+
risk patterns
<1ms
scanning
7
capability types
Per-agent
control
Agent MarketplaceNew Launch

29 Pre-Built Agents.
Unlimited Custom Agents.
One Marketplace.

Deploy specialized AI agents instantly or build custom agents from your codebase in minutes. Run up to 20 agents simultaneously — 20 is the ceiling, not the default; start at two — powered by Claude Code and Codex.

29
Pre-configured Agents
20
Parallel Session Ceiling
Unlimited
Custom Agents
NEW

Feature Orchestrator

Workflow Automation

Coordinates complex feature development across multiple agents, managing dependencies and ensuring cohesive delivery.

NEW

Bugfix Orchestrator

Issue Resolution

Triages, diagnoses, and coordinates bug fixes with automated root cause analysis and regression testing.

NEW

Coverage Orchestrator

Test Automation

Analyzes codebase coverage gaps and orchestrates test generation to meet quality thresholds systematically.

NEW

E2E Test Orchestrator

End-to-End Testing

Orchestrates end-to-end test suites across services, coordinating test generation, execution, and failure analysis.

Frontend Developer

React & Vue

Builds responsive, accessible UI components with modern frameworks, state management, and design system integration.

Backend Developer

Node.js & Python

Architects scalable server-side applications with RESTful APIs, authentication, and database integrations.

TypeScript Developer

Type Safety

Implements robust type systems, generic patterns, and compile-time safety for enterprise-grade applications.

API Developer

REST & GraphQL

Designs and implements robust API architectures with versioning, rate limiting, and comprehensive documentation.

Database Designer

SQL & NoSQL

Designs optimized database schemas, writes efficient queries, and implements migration strategies.

NEW

AI Engineer

ML Integration

Integrates machine learning models, implements AI pipelines, and optimizes inference performance.

Systems Architect

Infrastructure

Designs distributed systems, microservices architectures, and scalable cloud infrastructure patterns.

DevOps Engineer

CI/CD Pipelines

Automates deployment pipelines, manages infrastructure as code, and implements monitoring solutions.

Mobile Developer

React Native

Creates cross-platform mobile apps with native performance, handling navigation, state, and device APIs.

Code Reviewer

Best Practices

Performs thorough code reviews with actionable feedback on architecture, performance, and maintainability.

Code Debugger

Root Cause Analysis

Systematically traces bugs to their source, analyzes stack traces, and implements targeted fixes.

NEW

Code Security Auditor

OWASP & CVE

Identifies security vulnerabilities, implements fixes for OWASP Top 10, and ensures secure coding practices.

Code Standards Enforcer

Linting & Formatting

Enforces consistent code style, naming conventions, and architectural patterns across your codebase.

Code Refactor

Clean Architecture

Transforms legacy code into clean, maintainable patterns while preserving functionality and test coverage.

Code Documenter

API Docs & JSDoc

Generates comprehensive documentation, API references, and inline comments following best practices.

Content Strategist

Technical Writing

Creates engaging technical content, user guides, and developer documentation with clear messaging.

NEW

Visual Storyteller

Infographics

Transforms complex data and concepts into compelling visual narratives and presentation materials.

Marketing Website Developer

Landing Pages

Builds high-converting marketing sites with SEO optimization, analytics integration, and A/B testing.

Analytics Optimizer

Data Insights

Implements tracking solutions, analyzes user behavior, and provides data-driven optimization recommendations.

Accessibility Auditor

WCAG Compliance

Ensures WCAG 2.1 AA compliance with semantic HTML, ARIA labels, and comprehensive keyboard navigation.

Performance Optimizer

Core Web Vitals

Identifies bottlenecks and implements optimizations for LCP, FID, and CLS to boost user experience.

Test Engineer

E2E & Unit Tests

Designs comprehensive test strategies with unit, integration, and end-to-end test coverage.

Data Migration Specialist

ETL & Sync

Safely migrates data between systems with validation, rollback plans, and zero-downtime strategies.

NEW

Product Analyst

Metrics & Insights

Analyzes product metrics, user behavior patterns, and market data to drive informed product decisions.

NEW

AI Strategist

AI Integration

Plans and designs AI feature integrations, evaluates model capabilities, and architects intelligent automation workflows.

Custom Agents

Build Custom Agents From Your Codebase

CrewDeck automatically discovers your project structure, conventions, and patterns to generate specialized agents tailored to your codebase in minutes.

  • Auto-discovers coding patterns and conventions
  • Learns your tech stack and dependencies
  • Understands your project architecture
  • Respects your team's established practices
  • Deep Claude Code and Codex integration for maximum agent performance
Create Custom Agent
agent-builder
$ crewdeck discover ./my-project
Analyzing codebase...
Found: React 18, TypeScript, TailwindCSS
Found: 847 components, 234 tests
Detected patterns: Atomic Design, REST API
Generating custom agent config...
Success! Created: my-project-agent

Community Marketplace

Coming Soon

Share and discover agent configurations with community ratings and install counts. Browse agents built by other engineering teams, sorted by popularity and rating.

Loved by Engineering Teams

See what developers and engineering leaders are saying

“CrewDeck has transformed how we ship features. Our AI agents handle 40% of routine tasks, letting the team focus on architecture and innovation.”

Sarah Chen
CTO
TechStartup Inc.

“Finally, a tool that bridges the gap between project management and AI coding assistants. Running Claude Code agents side by side in isolated worktrees is a game-changer for parallel development.”

Marcus Rodriguez
Engineering Manager
DevScale

“We reduced our PR turnaround time by 60%. The AI agents understand our codebase patterns and deliver production-ready code consistently.”

Emily Watson
Lead Developer
CloudNative Labs

Frequently Asked Questions

Everything you need to know about CrewDeck