Target Audience

Who Uses
KubeRocketAI?

Keep using your favorite IDE or Web Chat.Discover if KubeRocketAI is the right solution for your team.
🚀

Development Squad

2-5 developers seeking coordination
Git-versioned AI agents that sync with your codebase updates
IDE for quick fixes, Web Chat for deep architecture discussions
No plugins needed — works in Cursor, Claude Code, ChatGPT today

Cross-Functional Teams

5-20 people with dedicated SDLC roles
Role-specific agents (BA, QA, PM, Dev) that share project context
Markdown configs evolve with your team's knowledge and standards
Same agent works in IDE for coding, Web Chat for planning sessions
🏢

Enterprise Organizations

20+ teams with custom frameworks
Framework-as-Code templates for internal libraries and patterns
Git-based sharing of successful AI workflows across streams
Governance controls with transparency — no black-box AI decisions

Framework Value Proposition

Built by developers, for developers - addressing real-world AI workflow challenges
Current RealityBuilt by developers experiencing these exact frustrations daily
Proven ApproachUses familiar DevOps patterns (YAML configs, Git workflows, validation)
Immediate BenefitSame agent definition works in IDE and can be bundled for web chat tools
Future GrowthMore teams adopt systematic AI workflows as AI tools become standard

Ready to Get Started?

Choose your path based on your team size and explore how KubeRocketAI works.
🚀
Development Squad
Start with shared team configurations
Quick Start Guide
Cross-Functional Teams
Explore role-based AI coordination features
View Architecture
🏢
Enterprise Organizations
See multi-stream AI framework management
Explore GitHub

Frequently Asked Questions

Yes! KubeRocketAI enhances your existing AI workflow rather than replacing it:

Compatibility with:

  • GitHub Copilot Chat integration (via VS Code)
  • Web chat tools (ChatGPT, Claude, Gemini) via bundle export
  • Existing prompt libraries and custom workflows
  • IDE-based AI assistants (Cursor, Claude Code, Windsurf)
  • How it works together:

  • KubeRocketAI provides structured agent personas for SDLC tasks
  • Your existing tools handle general coding assistance
  • The framework maintains project context in version-controlled files
  • Export bundles to web chat tools: krci-ai bundle --all --output project-context.md
  • Example Workflow:

    1. Use /pm agent in Claude Code for project planning

    2. Switch to GitHub Copilot for code completion

    3. Export framework context for web chat: krci-ai bundle --agent qa

    4. All context is maintained in your .krci-ai/ project files

    Learn how to set up integration with your existing tools in our Quick Start Guide.

    KubeRocketAI is designed for minimal learning curve with immediate productivity:

    Adoption Timeline:

  • Day 1: Install and start using basic agent commands
  • Week 1: Comfortable with all 8+ core SDLC agents (PM, Architect, Developer, QA, BA, PO, etc.)
  • Month 1: Customizing agents for team-specific workflows
  • Team Roles & Adoption Speed:

    Immediate (0-1 day):

  • Developers already using AI IDEs
  • Teams familiar with ChatGPT/Claude
  • Quick (1-3 days):

  • Traditional developers new to AI tools
  • Project managers learning AI-assisted planning
  • Gradual (1-2 weeks):

  • Teams transitioning from manual SDLC processes
  • Organizations establishing new development workflows
  • Learning Resources:

  • CLI Help: Built-in help for all commands (krci-ai --help)
  • Documentation: Comprehensive docs in docs-krci/ folder
  • Quick Start Guide: Get running in 3 minutes
  • Framework Examples: Real project templates and patterns
  • Agent Validation: Built-in validation to ensure correct usage
  • Getting Started:

    bash
    # Start with basic installation
    krci-ai install --ide=cursor
    
    # List available agents
    krci-ai list agents
    
    # Validate your setup
    krci-ai validate

    See which team profile matches yours in our Use Cases section.

    KubeRocketAI is Git-native with full version control integration:

    Agent Version Control:

  • Standard Git Workflow: Commit, branch, merge agent configurations
  • Diff Visibility: See exactly what changed in agent definitions (YAML files)
  • Team Collaboration: Share agent improvements through pull requests
  • Release Management: Tag stable agent versions for production use
  • File Structure:

    bash
    .krci-ai/
    ├── agents/
    │   ├── pm.yaml          # Product Manager agent
    │   ├── architect.yaml   # Software Architect agent
    │   ├── developer.yaml   # Developer agent
    │   ├── qa.yaml          # QA Engineer agent
    │   ├── ba.yaml          # Business Analyst agent
    │   └── po.yaml          # Product Owner agent
    ├── tasks/
    │   ├── create-prd.md    # Task definitions
    │   └── implement-feature.md
    ├── templates/
    │   └── story.md         # Output templates
    ├── data/
    │   └── coding-standards.md  # Reference data
    └── local/               # Project-specific overrides
        ├── tasks/
        ├── templates/
        └── data/

    Team Workflows:

  • Standard Git: Use branches, commits, and merges like any code project
  • Validation: Run krci-ai validate before commits
  • Pull Requests: Review agent changes through normal PR process
  • Custom Agents: Add project-specific agents in the agents directory
  • Learn more about framework components in our Architecture Guide.

    KubeRocketAI addresses documented inefficiencies in current AI-assisted development:

    Problems KubeRocketAI Solves:

    Current Pain Points:

  • 15-20% increase in code review cycles due to inconsistent AI outputs
  • 60% of web-based AI interactions produce generic responses requiring manual adaptation
  • 40% of organizations lack AI change tracking for compliance
  • Token limit failures affect 20% of AI deployments
  • KubeRocketAI Solutions:

  • Consistent Outputs: Standardized agent personas reduce review cycles
  • Project Context: AI responses align with your architecture and standards
  • Version Control: Full Git integration for AI change tracking
  • Token Management: Built-in validation prevents context limit failures
  • Framework Benefits:

  • Standardized Workflow: 8+ SDLC agents (PM, Architect, Developer, QA, BA, PO, etc.)
  • Team Coordination: Shared agent definitions across team members
  • Knowledge Retention: Capture team expertise in version-controlled agents
  • Quality Assurance: krci-ai validate ensures framework integrity
  • Competitive Advantages:

  • AI-Native Methodology: Designed for AI-enhanced development
  • Scalable Framework: Grows with your team and projects
  • Future-Ready: Prepared for evolving AI development tools
  • Adoption Benefits:

    Focus on reducing inefficiencies and standardizing AI interactions rather than specific percentage improvements.

    Discover if your team profile matches our target users in Use Cases.