← Back to Docs20250627

105 IMPLEMENTATION ROADMAP

Documentation for 105_IMPLEMENTATION_ROADMAP from the Foundation repository.

Foundation OS Implementation Roadmap

Version 1.0 - Detailed Execution Plan
Date: June 27, 2025

Executive Summary

This roadmap provides a comprehensive, phase-by-phase execution plan for transforming the current Foundation/MABEAM monolith into a world-class, dependency-based multi-agent platform. The transformation prioritizes risk mitigation, maintains system stability, and enables incremental delivery of value.

Total Estimated Timeline: 16-20 weeks
Resource Requirements: 2-3 senior developers
Risk Level: Medium (mitigated through incremental approach)

Project Overview

Current State

  • Foundation: Robust BEAM infrastructure with some agent-specific logic in foundation.mabeam
  • MABEAM: Partially implemented with architectural inconsistencies (per docs 005-017)
  • Jido Ecosystem: Mature, standalone libraries (jido, jido_action, jido_signal)
  • DSPEx/ElixirML: Working ML platform needing better agent integration

Target State

  • Foundation: Pure BEAM kernel focused on infrastructure services
  • FoundationJido: Clean integration layer bridging Foundation with Jido libraries
  • DSPEx: Enhanced ML platform with full multi-agent coordination capabilities
  • Unified error system using Foundation.Types.Error
  • Production-ready observability and deployment

Phase 1: Foundation Purification & Dependency Setup

Duration: 3-4 weeks
Risk: Low
Prerequisites: None

Objectives

  • Purify Foundation to be domain-agnostic BEAM kernel
  • Add Jido dependencies to the project
  • Create error standardization framework
  • Establish clean architectural boundaries

Deliverables

Week 1-2: Foundation Cleanup

  • 1.1 Deprecate foundation.mabeam namespace

    • Mark all lib/foundation/mabeam/ modules as deprecated
    • Add deprecation warnings to all public functions
    • Create migration notices pointing to future FoundationJido equivalents
    • Exit Criteria: All foundation.mabeam modules marked deprecated, warnings added

  • 1.2 Error System Consolidation

    • Complete merge of foundation.types.enhanced_error into foundation.types.error
    • Remove lib/foundation/types/enhanced_error.ex (per audit doc 013)
    • Update all Foundation modules to use unified error system
    • Exit Criteria: Single error system, enhanced_error.ex removed, all tests passing

  • 1.3 Add Jido Dependencies

    • Add jido, jido_action, jido_signal to mix.exs
    • Configure dependency versions and constraints
    • Update boundary configuration to prepare for integration layer
    • Exit Criteria: Dependencies resolve, project compiles, boundary checks pass

Week 3-4: Infrastructure Enhancement

  • 1.4 Foundation Service Enhancement

    • Enhance Foundation.ProcessRegistry with agent-aware metadata capabilities
    • Improve Foundation.Telemetry for multi-layer observability
    • Add coordination primitives to Foundation.Infrastructure
    • Exit Criteria: Enhanced services, backward compatibility maintained

  • 1.5 Boundary Establishment

    • Implement .boundary.exs configuration
    • Add boundary checking to CI pipeline
    • Create boundary violation detection and reporting
    • Exit Criteria: Architectural boundaries enforced, violations detected automatically

Success Criteria

  • All foundation.mabeam modules deprecated with clear migration path
  • Single, unified error system across Foundation
  • Jido dependencies successfully integrated
  • Enhanced Foundation services ready for integration layer
  • Architectural boundaries enforced via tooling

Risk Mitigation

  • Backwards Compatibility: Deprecated modules remain functional during transition
  • Testing: Comprehensive test suite maintained throughout refactoring
  • Rollback Plan: Git branches allow quick rollback to pre-refactor state

Phase 2: Integration Layer Development

Duration: 4-5 weeks
Risk: Medium
Prerequisites: Phase 1 complete

Objectives

  • Build FoundationJido integration layer
  • Migrate coordination logic from foundation.mabeam
  • Establish bridges between Foundation services and Jido libraries
  • Create comprehensive test coverage for integration points

Deliverables

Week 5-6: Core Integration Architecture

  • 2.1 FoundationJido Module Structure

    • Create lib/foundation_jido/ directory structure
    • Implement FoundationJido.Application supervisor
    • Build agent registry adapter (FoundationJido.Agent.RegistryAdapter)
    • Exit Criteria: Integration layer foundation established, basic agent registration working

  • 2.2 Error Bridge Implementation

    • Implement FoundationJido.Agent.ErrorBridge
    • Standardize Jido errors to use Foundation.Types.Error
    • Create error conversion utilities and helpers
    • Exit Criteria: All Jido interactions use Foundation error system

Week 7-8: Signal and Action Integration

  • 2.3 Signal Integration Layer

    • Build FoundationJido.Signal.FoundationDispatch adapter
    • Implement signal-to-event bridge (FoundationJido.Signal.EventBridge)
    • Add infrastructure integration for HTTP adapters
    • Exit Criteria: JidoSignal fully integrated with Foundation services

  • 2.4 Action Integration Layer

    • Create Foundation-aware actions (FoundationJido.Action.FoundationActions)
    • Implement action telemetry integration
    • Build infrastructure service actions
    • Exit Criteria: JidoAction uses Foundation services for resilience and observability

Week 9: Coordination Migration

  • 2.5 Coordination Protocol Migration
    • Move auction/market logic from foundation.mabeam to FoundationJido.Coordination
    • Refactor coordination protocols to use Jido agent framework
    • Implement multi-agent orchestration capabilities
    • Exit Criteria: All coordination logic migrated, multi-agent workflows functional

Success Criteria

  • Complete FoundationJido integration layer operational
  • All Jido libraries integrated with Foundation services
  • Coordination protocols successfully migrated
  • Comprehensive test coverage for all integration points
  • No regression in existing Foundation functionality

Risk Mitigation

  • Integration Testing: Extensive testing at each integration boundary
  • Feature Flags: Gradual rollout of integration features
  • Monitoring: Enhanced telemetry during integration development
  • Expert Consultation: Review with Jido library maintainers if needed

Phase 3: DSPEx Enhancement & Multi-Agent Intelligence

Duration: 3-4 weeks
Risk: Medium
Prerequisites: Phase 2 complete

Objectives

  • Enhance DSPEx with full multi-agent capabilities
  • Implement DSPEx-Jido bridge
  • Create ML-specific agent types and coordination patterns
  • Enable multi-agent teleprompters and optimization

Deliverables

Week 10-11: DSPEx-Jido Integration

  • 3.1 Program-Agent Bridge

    • Implement DSPEx.Jido.ProgramAgent (DSPEx programs as Jido agents)
    • Create program lifecycle management via agent supervision
    • Build program coordination capabilities
    • Exit Criteria: DSPEx programs run as first-class Jido agents

  • 3.2 ML Action Library

    • Create DSPEx.Jido.TeleprompterActions (optimization as actions)
    • Implement DSPEx.Jido.MLSignals for ML workflow communication
    • Build ML-specific signal patterns and dispatchers
    • Exit Criteria: ML workflows fully integrated with Jido action/signal systems

Week 12-13: Multi-Agent Coordination

  • 3.3 Variable System Integration

    • Bridge DSPEx variable system with FoundationJido coordination
    • Implement distributed variable optimization across agent teams
    • Create variable-aware coordination protocols
    • Exit Criteria: Variables coordinate multi-agent optimization workflows

  • 3.4 Advanced Teleprompters

    • Implement multi-agent SIMBA optimization
    • Create BEACON integration for rapid team composition
    • Build agent performance feedback loops
    • Exit Criteria: Multi-agent optimization algorithms operational

Success Criteria

  • DSPEx programs run as native Jido agents
  • ML workflows use action/signal patterns consistently
  • Multi-agent variable optimization functional
  • Advanced teleprompters enable team-based optimization
  • Full integration testing passes

Risk Mitigation

  • ML Domain Expertise: Ensure ML workflows remain scientifically sound
  • Performance Testing: Validate optimization algorithm performance
  • Backward Compatibility: Maintain existing DSPEx API surface
  • Documentation: Extensive examples and guides for new capabilities

Phase 4: Production Hardening & Observability

Duration: 3-4 weeks
Risk: Low
Prerequisites: Phase 3 complete

Objectives

  • Implement production-grade observability
  • Create comprehensive deployment automation
  • Establish performance benchmarks and monitoring
  • Complete documentation and migration guides

Deliverables

Week 14-15: Observability & Telemetry

  • 4.1 Unified Telemetry Architecture

    • Implement cross-layer metrics collection
    • Create distributed tracing for multi-agent workflows
    • Build performance monitoring dashboards
    • Exit Criteria: Complete observability across all system layers

  • 4.2 Error Tracking & Alerting

    • Implement error aggregation and analysis
    • Create intelligent alerting for system health
    • Build error correlation across agent teams
    • Exit Criteria: Production-grade error tracking and alerting

Week 16: Deployment & Documentation

  • 4.3 Deployment Automation

    • Create production deployment scripts and configurations
    • Implement health checks and readiness probes
    • Build deployment verification and rollback procedures
    • Exit Criteria: Automated, reliable production deployments

  • 4.4 Complete Documentation Suite

    • Finalize migration guides and API documentation
    • Create comprehensive examples and tutorials
    • Build troubleshooting guides and runbooks
    • Exit Criteria: Complete documentation enabling team adoption

Success Criteria

  • Production-grade observability and monitoring
  • Automated deployment with health verification
  • Complete documentation and migration guides
  • Performance benchmarks established and met
  • System ready for production use

Risk Mitigation

  • Load Testing: Comprehensive performance validation under realistic loads
  • Security Review: Security audit of all integration points
  • Disaster Recovery: Backup and recovery procedures tested
  • Team Training: Ensure team can operate and maintain the new system

Phase 5: Legacy Cleanup & Optimization (Optional)

Duration: 2-3 weeks
Risk: Low
Prerequisites: Phase 4 complete, system stable in production

Objectives

  • Remove deprecated foundation.mabeam code
  • Optimize performance based on production metrics
  • Implement advanced features and optimizations
  • Complete transition to target architecture

Deliverables

  • 5.1 Legacy Code Removal: Remove all deprecated foundation.mabeam modules
  • 5.2 Performance Optimization: Apply optimizations based on production data
  • 5.3 Advanced Features: Implement nice-to-have features and enhancements
  • 5.4 Architecture Validation: Confirm target architecture fully achieved

Resource Requirements

Team Composition

  • Senior Elixir Developer (Lead): Overall architecture, complex integrations
  • Senior Elixir Developer: Foundation and integration layer development
  • ML Engineer: DSPEx enhancements and multi-agent optimization
  • DevOps Engineer (Part-time): Deployment automation and observability

Infrastructure Requirements

  • Development Environment: Staging environment mimicking production
  • CI/CD Pipeline: Enhanced for boundary checking and integration testing
  • Monitoring Stack: Prometheus, Grafana, or equivalent for observability
  • Testing Infrastructure: Load testing and performance benchmarking capabilities

Risk Assessment & Mitigation

High-Risk Items

  1. Integration Complexity: Mitigated by incremental development and extensive testing
  2. Performance Degradation: Mitigated by continuous benchmarking and optimization
  3. Team Knowledge Gap: Mitigated by documentation and knowledge transfer

Medium-Risk Items

  1. Jido Library Changes: Mitigated by version pinning and compatibility testing
  2. Production Migration: Mitigated by blue-green deployment and rollback procedures
  3. ML Algorithm Accuracy: Mitigated by validation against existing implementations

Contingency Plans

  • Phase Delays: Each phase can be extended by 1-2 weeks if needed
  • Technical Blockers: Expert consultation budget allocated for complex issues
  • Production Issues: Immediate rollback procedures and hotfix capabilities

Success Metrics

Technical Metrics

  • Test Coverage: >95% across all integration points
  • Performance: No degradation vs. current system, 20%+ improvement in coordination
  • Reliability: 99.9% uptime for all Foundation services
  • Integration Quality: Zero boundary violations, clean separation of concerns

Business Metrics

  • Development Velocity: 30%+ improvement in new agent development time
  • System Maintainability: 50%+ reduction in coordination-related bugs
  • Platform Adoption: Successful migration of all existing DSPEx workflows
  • Documentation Quality: Complete onboarding capability for new team members

Conclusion

This roadmap provides a low-risk, incremental path to achieving the world-class multi-agent platform described in the architectural vision. Each phase builds upon the previous one while maintaining system stability and enabling early value delivery.

The dependency-based approach ensures we leverage the Jido ecosystem as intended while creating a robust, maintainable foundation for long-term evolution. The emphasis on observability, testing, and documentation ensures the resulting system can be operated and maintained effectively in production.

Next Steps: Begin Phase 1 with Foundation purification and dependency setup. The success of this transformation depends on disciplined execution of each phase while maintaining focus on the ultimate goal of creating the world’s most sophisticated BEAM-native multi-agent platform.