Kumeo Implementation Roadmap

This document outlines the step-by-step approach to implementing the Kumeo platform, from initial design to production deployment.

Phase 1: Foundation (Months 1-2)

1.1 DSL Design

  • Define core syntax and grammar
  • Design type system for agent interactions
  • Create formal language specification
  • Develop parser using LALRPOP or similar tool
  • Implement abstract syntax tree (AST) representation

1.2 Compiler Foundation

  • Set up compiler project structure
  • Implement lexer and parser
  • Develop semantic validation
  • Create initial symbol table management
  • Define intermediate representation (IR)

1.3 Runtime Foundations

  • Define runtime core APIs
  • Implement basic event handling
  • Create agent abstraction layer
  • Set up NATS client integration
  • Develop simple workflow executor
  • Add Python runtime support
    • BaseAgent implementation
    • RuntimeClient for Python
    • Message serialization/deserialization
    • Lifecycle management
    • Error handling and recovery

Phase 2: Core Implementation (Months 3-5)

2.1 Compiler Development

  • Implement multi-language code generation
    • Rust generation for LLM agents and basic operations
    • Python generation for ML models and Bayesian networks
    • Template system for code generation
    • Inter-language communication layer
  • Add type checking and validation
  • Create Kubernetes manifest generator
  • Implement optimization passes
  • Add support for context variables

2.2 Agent Implementation

  • Develop LLM agent interface
    • OpenAI integration
    • Ollama integration
  • Implement ML model agent
    • ONNX runtime support
    • Scikit-learn model loading
  • Create Bayesian network agent
  • Implement decision matrix agent
  • Develop human-in-the-loop agent

2.3 Runtime Enhancement

  • Implement error handling and retry mechanisms
  • Add monitoring and logging
  • Create deployment pipelines
  • Develop configuration management
  • Implement security features

Phase 3: Advanced Features (Months 6-8)

3.1 DSL Extensions

  • Add subworkflow support
  • Implement conditional execution
  • Create parallel processing constructs
  • Add dynamic agent configuration
  • Develop error handling patterns

3.2 Runtime Enhancements

  • Implement state persistence
  • Add distributed tracing
  • Create advanced monitoring
  • Implement performance optimizations
  • Add resource management

3.3 Kubernetes Integration

  • Develop custom resource definitions (CRDs)
  • Create Kubernetes operators
  • Implement auto-scaling
  • Add high-availability features
  • Develop multi-cluster support

Phase 4: UI and User Experience (Months 9-10)

4.1 Visual Editor

  • Design UI wireframes and prototypes
  • Implement workflow canvas
  • Create agent configuration panels
  • Develop property editors
  • Add validation and feedback

4.2 Monitoring Dashboard

  • Design monitoring UI
  • Implement real-time updates
  • Create visualization components
  • Add filtering and search
  • Develop alerting configuration

4.3 Documentation

  • Create comprehensive API documentation
  • Write user guides
  • Develop tutorials and examples
  • Create video walkthroughs
  • Implement interactive documentation

Phase 5: Testing and Refinement (Months 11-12)

5.1 Testing Infrastructure

  • Develop unit test framework
  • Create integration test suite
  • Implement end-to-end testing
  • Add performance benchmarks
  • Create stress testing tools

5.2 Example Workflows

  • Develop fraud detection example
  • Create customer service bot
  • Implement data processing pipeline
  • Add content generation workflow
  • Create decision support system

5.3 Beta Program

  • Recruit beta testers
  • Gather and implement feedback
  • Fix bugs and issues
  • Improve documentation
  • Refine user experience

Phase 6: Production Release (Month 12+)

6.1 Launch Preparation

  • Finalize documentation
  • Complete all testing
  • Prepare marketing materials
  • Create release notes
  • Plan support infrastructure

6.2 Initial Release

  • Deploy production environment
  • Release v1.0
  • Monitor adoption and usage
  • Provide initial support
  • Gather feedback for future iterations

6.3 Ongoing Development

  • Plan v1.1 features
  • Prioritize community feedback
  • Expand agent ecosystem
  • Improve performance
  • Add enterprise features

Technical Implementation Details

DSL Grammar Development 

workflow ::= 'workflow' IDENTIFIER '{' workflow_body '}'
workflow_body ::= source target context? agents
source ::= 'source:' source_def
source_def ::= NATS_SOURCE | HTTP_SOURCE | ...

Compiler Pipeline

  1. Parsing: .kumeo files → Parse Tree
  2. AST Construction: Parse Tree → AST
  3. Semantic Analysis: AST + Symbol Tables → Validated AST
  4. Optimization: Validated AST → Optimized AST
  5. Code Generation: Optimized AST → Rust code + K8s YAML

Runtime Architecture 

                     ┌─────────────┐
                     │ API Gateway │
                     └──────┬──────┘
                            │
┌─────────┐          ┌──────▼──────┐
│ Agents  │◀─────────┤  Workflow   │
└─────────┘          │   Engine    │
     ▲               └──────┬──────┘
     │                      │
     │               ┌──────▼──────┐
     └───────────────┤ Event Router │
                     └──────┬──────┘
                            │
                     ┌──────▼──────┐
                     │    NATS     │
                     └─────────────┘

Risk Assessment and Mitigation

Risk Impact Likelihood Mitigation
Complex language design creates adoption barriers High Medium Focus on simplicity, provide examples and templates
Runtime performance issues High Medium Early performance testing, optimization passes
Integration challenges with external models Medium High Develop robust adapter patterns and fallbacks
Kubernetes complexity Medium Medium Abstract deployment details, provide managed options
Security vulnerabilities High Low Regular security audits, principle of least privilege

Resource Requirements

Development Team

  • 2 Rust developers (compiler, runtime)
  • 1 Frontend developer (UI)
  • 1 DevOps engineer (Kubernetes, NATS)
  • 1 Technical writer (documentation)

Infrastructure

  • Development environment
  • CI/CD pipeline
  • Testing infrastructure
  • Demo environment
  • Documentation hosting

Success Metrics

  • Compiler successfully generates valid Rust code for 95% of test cases
  • Runtime handles at least 1,000 events per second per node
  • UI enables workflow creation in under 10 minutes for new users
  • At least 10 example workflows demonstrating different use cases
  • Documentation covers 100% of public APIs and features

Conclusion

This implementation roadmap provides a structured approach to building the Kumeo platform over approximately 12 months. The phased approach allows for incremental development and testing, with clear milestones and deliverables at each stage.