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CEO Development TODO ​

Context: CEO (Context/Execution Orchestrator) is the cognitive executive that translates human intents into structured resource allocation. This document tracks implementation priorities and features needed to reach production readiness.

High Priority (v0 MVP) ​

Intent Processing Engine ​

  • [ ] Natural Language Intent Parser

    • Implement semantic intent classification from user queries
    • Support common query patterns (search, explain, debug, implement)
    • Handle ambiguous queries with confidence scoring and clarification requests
    • Entity extraction for technical terms, file names, error messages

  • [ ] Intent Validation & Enrichment

    • Validate extracted intents against supported operation types
    • Context enrichment from session history and user preferences
    • Intent complexity estimation for budget calculation
    • Fallback handling for unsupported or unclear intents

  • [ ] Multi-modal Input Support (Basic)

    • Text query processing (primary)
    • Code snippet context integration
    • Error message interpretation and categorization
    • File path and stack trace parsing

Budget Management System ​

  • [ ] Dynamic Budget Allocation

    • Token budget calculation based on query complexity and intent type
    • Time budget estimation with urgency detection and user preferences
    • Cost budget management with provider cost awareness
    • Budget distribution across L1/L2/L4 providers

  • [ ] Resource Optimization

    • Query complexity scoring algorithm (simple → complex scale)
    • Historical performance data integration for budget prediction
    • Budget adjustment based on user feedback and satisfaction
    • Emergency budget expansion for critical tasks

  • [ ] Budget Policy Engine

    • Configurable budget policies per user tier (free, pro, enterprise)
    • Budget overrun protection with graceful degradation
    • Cost tracking and reporting for transparency
    • Budget preemption for time-sensitive queries

Error Handling & Recovery ​

  • [ ] Structured Error Processing

    • ACS error interpretation and user-friendly translation
    • Error recovery strategy selection (retry, fallback, user choice)
    • Error context preservation for debugging and improvement
    • Escalation paths for unrecoverable errors

  • [ ] Intelligent Retry Logic

    • Contextual retry decisions based on error type and cause
    • Exponential backoff with jitter for provider failures
    • Budget adjustment on retries (reduce scope, extend time)
    • Retry history tracking for pattern recognition

  • [ ] Graceful Degradation Framework

    • Partial result presentation when components fail
    • Alternative execution paths when primary approach fails
    • User notification of degraded service with explanation
    • Quality indicators for degraded responses

Medium Priority (v0.1) ​

Advanced Intent Understanding ​

  • [ ] Context-Aware Parsing

    • Session context integration for query disambiguation
    • Project context awareness from L2 Project Library
    • User expertise level detection for response complexity tuning
    • Conversation flow tracking for multi-turn queries

  • [ ] Intent Learning & Improvement

    • User feedback collection for intent classification improvement
    • Intent pattern recognition and automatic categorization
    • Personalization based on user query patterns and preferences
    • A/B testing framework for intent processing improvements

Sophisticated Budget Management ​

  • [ ] Predictive Budget Modeling

    • Machine learning models for budget prediction based on query features
    • Historical performance analysis for budget optimization
    • Real-time budget adjustment based on provider performance
    • Multi-objective optimization (quality vs. cost vs. latency)

  • [ ] Resource Pool Management

    • Provider capacity management and load balancing
    • Resource reservation for high-priority queries
    • Dynamic provider selection based on current load and performance
    • Cost optimization across multiple provider options

Performance & Observability ​

  • [ ] Comprehensive Metrics Collection

    • Intent processing accuracy and latency metrics
    • Budget efficiency and resource utilization tracking
    • Error rate and recovery success metrics by query type
    • User satisfaction and response quality indicators

  • [ ] Real-time Monitoring & Alerting

    • Performance dashboards for CEO component health
    • Alerting for error rate spikes or performance degradation
    • Resource utilization monitoring and capacity planning
    • User experience metrics and satisfaction tracking

Low Priority (v0.2+) ​

Advanced Features ​

  • [ ] Multi-modal Input Processing

    • Image/screenshot analysis for UI debugging queries
    • Audio input processing for voice-based queries
    • Document upload and analysis for context enrichment
    • Real-time screen sharing integration for debugging

  • [ ] Collaborative Query Processing

    • Multi-user query coordination for team collaboration
    • Query sharing and result distribution to team members
    • Workspace-aware context and resource sharing
    • Collaborative debugging and problem-solving workflows

Intelligence & Learning ​

  • [ ] Advanced Personalization

    • Individual user models for query prediction and suggestion
    • Learning from user behavior patterns and preferences
    • Proactive assistance based on project context and patterns
    • Customizable response styles and detail levels

  • [ ] Predictive Assistance

    • Anticipatory resource preparation based on project activity
    • Suggestion generation for follow-up queries and tasks
    • Anomaly detection in code/project patterns for proactive alerts
    • Integration with development workflow tools for context awareness

Documentation & Developer Experience ​

Implementation Guides ​

  • [ ] Developer Documentation

    • Comprehensive API documentation with examples
    • Integration patterns for different client types
    • Error handling best practices and common patterns
    • Performance optimization guides and troubleshooting

  • [ ] SDK & Tooling

    • Client SDKs for popular languages (TypeScript, Python, Go)
    • CLI tools for testing and development
    • Mock CEO implementation for testing dependent components
    • Integration testing utilities and test fixtures

Architecture Documentation ​

  • [ ] Design Decision Records (ADR)

    • Intent processing algorithm choices and trade-offs
    • Budget allocation strategy rationale
    • Error handling philosophy and implementation decisions
    • Performance vs. accuracy trade-off analysis

  • [ ] Integration Patterns Documentation

    • Common integration scenarios with detailed examples
    • Anti-patterns and pitfalls to avoid
    • Scaling considerations and deployment patterns
    • Security and privacy implementation guides

Research & Exploration ​

Advanced Cognitive Models ​

  • [ ] Intent Understanding Research

    • Advanced NLP models for complex technical query understanding
    • Domain-specific language models for programming context
    • Multi-turn conversation understanding and context preservation
    • Intent ambiguity resolution using clarifying questions

  • [ ] Resource Allocation Optimization

    • Multi-objective optimization for quality/cost/latency trade-offs
    • Reinforcement learning for budget allocation improvement
    • Game theory approaches for resource competition resolution
    • Economic models for resource pricing and allocation

Future Architecture ​

  • [ ] Distributed CEO Architecture

    • Multi-region deployment for latency optimization
    • Load balancing and failover between CEO instances
    • Shared learning across distributed CEO deployments
    • Consistency models for distributed intent processing

  • [ ] Cognitive Architecture Integration

    • Integration with external cognitive services and models
    • Plugin architecture for extensible intent processing
    • Integration with reasoning and planning systems
    • Connection to external knowledge and expertise networks

Implementation Notes ​

Current Status (v0) ​

  • ✅ Comprehensive architecture specification (32,000+ lines)
  • ✅ API contracts defined and documented
  • ✅ Error handling patterns designed
  • ✅ Integration points specified
  • 🚧 Core intent processing implementation
  • 🚧 Budget management system development

Success Metrics ​

  • Intent Accuracy: > 95% correct classification of user intents
  • Budget Efficiency: < 10% resource waste through optimal allocation
  • Response Latency: < 200ms end-to-end processing (excluding ACS)
  • Error Recovery: > 90% successful automatic recovery from failures
  • User Satisfaction: > 4.5/5 rating for response relevance and quality

Dependencies ​

  • ACS Integration: Requires stable ACS API for downstream communication
  • Provider APIs: L1/L2/L4 providers must be available for end-to-end testing
  • Authentication System: User identity and session management infrastructure
  • Monitoring Infrastructure: Observability stack for metrics and alerting

Getting Started ​

  1. Review architecture in ./architecture.md for comprehensive design
  2. Study API contracts in ../api/internal.md for integration details
  3. Understand error patterns in ../errors.md for robust implementation
  4. Examine examples in ../../walkthrough.md for practical usage
  5. Start with high-priority implementation tasks above

For technical questions, refer to the detailed architecture specification or the orchestration overview in ../README.md.