GitHub Projects Integration for Engram Context Engine

Overview

The Engram Context Engine now integrates with GitHub Projects to enable comprehensive tracking of the Production-Grade Agentic System implementation. This integration allows Elena (Business Analyst) and Marcus (Project Manager) agents to actively manage tasks, track progress, and maintain visibility across all seven layers of agentic AI systems.

Creating the Story

To create a story about this integration, delegate to Sage through Elena or Marcus:

  • Via Elena: “Elena, please delegate to Sage to create a story about GitHub Projects integration…”
  • Via Marcus: “Marcus, please delegate to Sage to create a story about our GitHub Projects integration…”

See How to Create GitHub Projects Story for detailed instructions.

Integration Architecture

Seven Layers of Agentic AI Systems

The GitHub Projects integration supports tracking across all seven layers:

  1. Layer 1: Interaction - Generative UI, streaming, HITL
  2. Layer 2: Orchestration - Durable execution, multi-agent patterns, state management
  3. Layer 3: Cognition - LLM gateway, reasoning patterns, structured outputs
  4. Layer 4: Memory - GraphRAG, context optimization, data privacy
  5. Layer 5: Tools - MCP integration, sandboxed execution, validation
  6. Layer 6: Guardrails - Input/output guardrails, execution limits, compliance
  7. Layer 7: Observability - Distributed tracing, evaluation, cost governance

How GitHub Projects Applies to Each Layer

Layer 1: Interaction

  • Tasks Tracked: Generative UI components, streaming enhancements, HITL approval workflows
  • Progress Metrics: UI component completion, streaming latency improvements, approval workflow adoption
  • Agent Involvement: Elena (requirements), Marcus (timeline), Sage (visuals/documentation)

Layer 2: Orchestration

  • Tasks Tracked: Temporal workflow enhancements, multi-agent delegation patterns, state persistence
  • Progress Metrics: Workflow reliability, agent coordination success rate, state branching capabilities
  • Agent Involvement: Marcus (orchestration), Elena (requirements), Sage (architecture diagrams)

Layer 3: Cognition

  • Tasks Tracked: LLM gateway deployment, structured output enforcement, reasoning pattern implementation
  • Progress Metrics: Cost reduction from smart routing, validation success rate, reasoning trace quality
  • Agent Involvement: Marcus (gateway), Elena (validation), Sage (documentation)

Layer 4: Memory

  • Tasks Tracked: GraphRAG implementation, context compression, privacy controls
  • Progress Metrics: Knowledge graph size, context window efficiency, privacy compliance
  • Agent Involvement: Elena (requirements), Marcus (timeline), Sage (visualizations)

Layer 5: Tools

  • Tasks Tracked: MCP integration, sandboxed execution, tool validation
  • Progress Metrics: Tool execution safety, validation coverage, MCP adoption
  • Agent Involvement: Marcus (execution), Elena (validation), Sage (documentation)

Layer 6: Guardrails (CRITICAL)

  • Tasks Tracked: Input/output guardrails, execution limits, compliance mapping
  • Progress Metrics: Injection detection rate, cost limit effectiveness, compliance coverage
  • Agent Involvement: Elena (requirements/compliance), Marcus (execution), Sage (documentation)

Layer 7: Observability

  • Tasks Tracked: LLMOps platform, evaluation framework, cost governance
  • Progress Metrics: Trace coverage, eval scores, cost visibility
  • Agent Involvement: Marcus (platform), Elena (evaluation), Sage (reports)

Progress Tracking Mechanism

Task Lifecycle

  1. Task Creation
    • Agent creates GitHub issue for assigned task
    • Issue labeled with: production-grade-system, layer name, phase name
    • Issue assigned to agent (Elena/Marcus) via labels
  2. Task Execution
    • Agent updates issue with progress notes
    • Status tracked via issue state (open/in-progress)
    • Sub-tasks tracked via issue comments or checklists
  3. Task Completion
    • Agent closes issue when complete
    • Completion metrics aggregated for progress reporting
    • Story/visual artifacts linked in issue
  4. Progress Monitoring
    • Agents query get_project_status for metrics
    • System tracks completion percentage per layer
    • Critical path tasks highlighted

Progress Metrics

Overall Progress:

  • Total tasks: 21 tasks across 7 layers
  • Completed tasks: Tracked via closed issues
  • Progress percentage: (Completed / Total) × 100

Layer-Specific Metrics:

  • Tasks per layer: 2-4 tasks per layer
  • Completion rate per layer
  • Critical tasks (Layer 6) prioritized

Agent Workload:

  • Tasks assigned to Elena: ~10 tasks
  • Tasks assigned to Marcus: ~11 tasks
  • Delegation to Sage: Story/visual creation

Agent Capabilities

Elena (Business Analyst)

GitHub Tools Available:

  • create_github_issue - Create tasks for requirements/compliance work
  • update_github_issue - Update progress on assigned tasks
  • get_project_status - Check overall implementation progress
  • list_my_tasks - View assigned tasks
  • close_task - Mark tasks complete

Typical Workflow:

  1. Receives task assignment (e.g., Task 6.1: Input Guardrails)
  2. Creates GitHub issue with requirements details
  3. Updates issue as work progresses
  4. Closes issue when complete
  5. Delegates to Sage for documentation/stories

Marcus (Project Manager)

GitHub Tools Available:

  • create_github_issue - Create tasks for execution/orchestration work
  • update_github_issue - Update progress and status
  • get_project_status - Generate status reports
  • list_my_tasks - Track assigned work
  • close_task - Mark tasks complete

Typical Workflow:

  1. Creates timeline for phase
  2. Creates issues for assigned tasks
  3. Monitors progress across all layers
  4. Generates status reports from GitHub data
  5. Delegates to Sage for visualizations/timelines

Sage (Storyteller & Visualizer)

Delegation Workflow:

  1. Receives delegation from Elena or Marcus
  2. Creates story/visual via Temporal workflow
  3. Story stored in Zep memory
  4. Visual saved to docs folder
  5. Links provided back to delegating agent

System Awareness

Engram’s Awareness of Progress

The Engram system maintains awareness of GitHub Projects progress through:

  1. Agent Queries
    • Agents actively query project status
    • Progress metrics available in agent context
    • Agents can report on implementation status
  2. Memory Integration
    • Progress updates stored in Zep memory
    • Historical progress tracked over time
    • Agents can recall past progress states
  3. Workflow Integration
    • Temporal workflows can query GitHub status
    • Progress metrics influence workflow decisions
    • Status reports generated from GitHub data
  4. Frontend Visibility
    • Progress dashboards can query GitHub API
    • Real-time progress visualization
    • Layer-by-layer progress breakdown

Benefits

For Agents

  • Self-Awareness: Agents know their assigned tasks
  • Progress Tracking: Agents can report on their work
  • Coordination: Agents can see overall project status
  • Accountability: Clear ownership and progress visibility

For System

  • Transparency: Clear view of implementation progress
  • Metrics: Quantifiable progress tracking
  • Coordination: Multi-agent task coordination
  • Documentation: Automatic linking of artifacts

For Users

  • Visibility: See what agents are working on
  • Progress: Track implementation across layers
  • Artifacts: Access stories, visuals, and documentation
  • Confidence: Clear evidence of systematic progress

Implementation Status

Current State

✅ Completed:

  • GitHub API client implementation
  • Agent tools for GitHub interaction
  • Authorization model
  • Documentation

⏳ In Progress:

  • Initial task creation from implementation plan
  • Project setup and configuration

🔮 Future Enhancements:

  • GraphQL API for full project field support
  • Automated sync from implementation plan
  • Progress dashboard in frontend
  • GitHub App for fine-grained permissions

References

Last Updated: December 20, 2024