Multi-Agent Orchestration Research - Foundry Integration

Status: Research Complete
Last Updated: January 2026
Priority: High
Timeline: 3-4 weeks for implementation

Executive Summary

Multi-agent orchestration enables coordinated collaboration between multiple AI agents to execute complex workflows. Azure AI Foundry provides built-in orchestration capabilities that can enhance Engram’s multi-agent system (Elena, Marcus, Sage) for complex, multi-step tasks.

Key Findings:

✅ Foundry supports multi-agent workflows with shared context
✅ Automatic handoff handling and coordination
✅ Workflow visualization and observability
✅ Hybrid approach recommended: Foundry for complex workflows, Engram router for simple requests

What is Multi-Agent Orchestration?

Definition

Multi-agent orchestration is the coordination of multiple specialized AI agents to collaboratively execute complex tasks that require:

Sequential processing: Tasks that flow from one agent to another
Parallel processing: Multiple agents working simultaneously
Shared context: Agents accessing and updating common state
Dynamic routing: Intelligent task assignment based on agent capabilities

Industry Examples

1. IBM watsonx Orchestrate

Acts as supervisor, coordinating specialized agents
Routes requests to appropriate tools and LLMs
Supports React (exploration) and Plan-Act (structured flows)
Ensures workflows remain governed and observable

2. Temporal Multi-Agent Workflows

Provides orchestration for reliable multi-agent interactions
Manages complex stateful and stateless interactions
Ensures fault tolerance and retry logic
Suitable for AI applications requiring coordinated activities

3. AgentOrchestrationLayer

Visual workflow builder for multi-agent systems
Distributed state management
Dynamic task handoff
Supports LangGraph and AutoGen frameworks

Current Engram State

Existing Multi-Agent System

Engram’s Three Agents:

Elena (Dr. Elena Vasquez) - Business Analyst
- Requirements analysis
- Microsoft Graph integration (email, OneDrive)
- Business process documentation
Marcus (Marcus Chen) - Project Manager
- Project planning and timelines
- Resource allocation
- Risk assessment
Sage (Sage Meridian) - Storytelling & Visualization
- Technical documentation
- Story generation
- Visual diagram creation

Current Orchestration Approach

Engram Router (backend/agents/router.py):

✅ Agent selection based on query analysis
✅ Handoff detection via keyword matching
✅ Separate conversation threads per agent
✅ Agents can reference each other’s work via memory search

Limitations:

⚠️ Manual handoff detection (keyword-based)
⚠️ No shared context between agents
⚠️ No workflow visualization
⚠️ Complex multi-step tasks require user intervention

Example Current Flow:

User: "I need requirements for a new feature, then a project plan"
  ↓
Elena: Analyzes requirements → Suggests Marcus
  ↓
User: Manually switches to Marcus
  ↓
Marcus: Creates project plan → Suggests Sage
  ↓
User: Manually switches to Sage
  ↓
Sage: Creates documentation

Azure AI Foundry Orchestration Capabilities

Foundry Multi-Agent Features

1. Workflow Definition

Define multi-agent workflows declaratively
Specify agent roles and responsibilities
Define handoff conditions and rules
Configure shared context and state

2. Automatic Coordination

Built-in agent coordination logic
Automatic handoff handling
Shared context management
State synchronization

3. Workflow Visualization

Visual representation of agent interactions
Real-time workflow status
Observability and monitoring
Debugging and troubleshooting

4. Fault Tolerance

Automatic retry on failures
Error handling and recovery
Graceful degradation
State persistence

Proposed Integration Approach

Hybrid Strategy

Use Foundry For:

✅ Complex multi-agent workflows
✅ Sequential task chains (Elena → Marcus → Sage)
✅ Parallel agent collaboration
✅ Shared context requirements

Keep Engram Router For:

✅ Simple single-agent requests
✅ Direct agent selection
✅ Fast response times
✅ Existing handoff detection

Architecture

┌─────────────────┐
│   User Query    │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Task Analyzer  │
└────────┬────────┘
         │
    ┌────┴────┐
    │         │
    ▼         ▼
┌─────────┐ ┌──────────────────┐
│ Simple  │ │   Complex Task    │
│  Task   │ │                  │
└────┬────┘ └────────┬─────────┘
     │               │
     │               ▼
     │      ┌──────────────────┐
     │      │ Foundry Workflow │
     │      │   Orchestrator   │
     │      └────────┬─────────┘
     │               │
     │      ┌────────┴────────┐
     │      │                 │
     │      ▼                 ▼
     │  ┌───────┐        ┌───────┐
     │  │ Elena │ ────► │ Marcus │
     │  └───────┘        └───┬───┘
     │                        │
     │                        ▼
     │                   ┌───────┐
     │                   │ Sage  │
     │                   └───────┘
     │
     ▼
┌─────────────┐
│ Engram      │
│ Router      │
└─────────────┘

Use Cases

1. Requirements → Project Planning

User: "I need a new feature for user authentication"
  ↓
Foundry Workflow:
  1. Elena: Analyzes requirements
     - Creates requirements document
     - Identifies stakeholders
     - Defines acceptance criteria
  2. Automatic handoff to Marcus
  3. Marcus: Creates project plan
     - Timeline and milestones
     - Resource allocation
     - Risk assessment
  4. Automatic handoff to Sage
  5. Sage: Creates documentation
     - Technical specification
     - User story
     - Architecture diagram
  ↓
Result: Complete feature specification with plan and docs

2. Comprehensive Project Analysis

User: "Analyze our Q1 project portfolio"
  ↓
Foundry Workflow (Parallel):
  - Elena: Business analysis (requirements, ROI)
  - Marcus: Project management (timelines, resources)
  - Sage: Documentation (reports, visualizations)
  ↓
Result: Comprehensive analysis combining all perspectives

3. Multi-Step Feature Development

User: "Build a new dashboard feature"
  ↓
Foundry Workflow:
  1. Elena: Requirements gathering
  2. Marcus: Project planning
  3. Sage: Technical documentation
  4. Elena: Stakeholder communication
  5. Marcus: Progress tracking
  6. Sage: User guide creation
  ↓
Result: Complete feature from requirements to documentation

Implementation Plan

Phase 1: Research & Design (Week 1)

Tasks:

✅ Research Foundry workflow capabilities
✅ Design workflow patterns for Engram use cases
✅ Create workflow definitions
✅ Plan integration with Engram router

Deliverables:

Workflow design document
Integration architecture
Use case specifications

Phase 2: Foundry Workflow Creation (Week 2)

Tasks:

Create Foundry workflow with Elena, Marcus, Sage
Define handoff rules and conditions
Configure shared context
Test basic workflow execution

Implementation:

# Foundry workflow definition
workflow_definition = {
    "name": "engram-multi-agent-workflow",
    "agents": [
        {
            "id": "elena",
            "role": "business_analyst",
            "capabilities": ["requirements", "analysis", "stakeholder_communication"]
        },
        {
            "id": "marcus",
            "role": "project_manager",
            "capabilities": ["planning", "timeline", "resource_allocation"]
        },
        {
            "id": "sage",
            "role": "documentation_specialist",
            "capabilities": ["documentation", "visualization", "storytelling"]
        }
    ],
    "handoff_rules": [
        {
            "from": "elena",
            "to": "marcus",
            "condition": "requirements_complete",
            "trigger": "requirements_document_created"
        },
        {
            "from": "marcus",
            "to": "sage",
            "condition": "project_plan_created",
            "trigger": "timeline_established"
        }
    ],
    "shared_context": {
        "enabled": True,
        "scope": "workflow",
        "persistence": True
    }
}

Phase 3: Integration with Engram (Week 3)

Tasks:

Create Foundry workflow client
Integrate with Engram router
Implement task complexity detection
Add workflow execution logic

Implementation:

# backend/agents/foundry_orchestrator.py
class FoundryOrchestrator:
    """
    Orchestrates multi-agent workflows using Foundry.
    """
    
    async def execute_workflow(
        self,
        workflow_name: str,
        initial_query: str,
        context: EnterpriseContext
    ) -> WorkflowResult:
        """
        Execute a Foundry multi-agent workflow.
        """
        # Create workflow execution
        execution = await foundry_client.create_workflow_execution(
            workflow_name=workflow_name,
            initial_input=initial_query,
            context=context.to_dict()
        )
        
        # Monitor execution
        while not execution.is_complete():
            status = await execution.get_status()
            if status.has_handoff():
                # Log handoff
                logger.info(f"Handoff: {status.from_agent} → {status.to_agent}")
            
            await asyncio.sleep(1)
        
        # Get final result
        return await execution.get_result()

# backend/agents/router.py
async def route_and_execute(
    self, query: str, context: EnterpriseContext, agent_id: Optional[AgentId] = None
) -> tuple[str, EnterpriseContext, AgentId, Optional[str]]:
    """
    Route query - use Foundry workflow for complex tasks, Engram router for simple.
    """
    # Detect if task requires multi-agent workflow
    if self._requires_workflow(query):
        orchestrator = FoundryOrchestrator()
        result = await orchestrator.execute_workflow(
            workflow_name="engram-multi-agent-workflow",
            initial_query=query,
            context=context
        )
        return result.response, context, result.final_agent, None
    
    # Simple task - use existing router
    return await self._route_simple(query, context, agent_id)

def _requires_workflow(self, query: str) -> bool:
    """
    Detect if query requires multi-agent workflow.
    """
    complex_indicators = [
        "requirements and plan",
        "analyze and document",
        "complete project",
        "full analysis",
        "comprehensive",
    ]
    return any(indicator in query.lower() for indicator in complex_indicators)

Tasks:

Test workflow execution
Validate handoff logic
Test shared context
Performance optimization
Error handling

Test Cases:

Sequential Workflow: Elena → Marcus → Sage
Parallel Workflow: All agents working simultaneously
Conditional Handoff: Handoff based on conditions
Error Recovery: Handle agent failures
Context Sharing: Verify shared state

Benefits

✅ Enhanced Capabilities

Complex Workflows: Execute multi-step tasks automatically
Better Coordination: Agents work together seamlessly
Shared Context: Agents access common information
Automatic Handoffs: No manual intervention needed

✅ Improved User Experience

Single Request: User asks once, gets complete result
Faster Results: Parallel agent execution
Better Quality: Comprehensive analysis from all agents
Less Friction: No need to manually switch agents

✅ Operational Benefits

Observability: Workflow visualization
Debugging: Easy to trace agent interactions
Scalability: Foundry manages infrastructure
Reliability: Built-in fault tolerance

Challenges & Considerations

⚠️ Complexity

Learning Curve: Team needs to understand Foundry workflows
Debugging: Multi-agent workflows can be complex to debug
State Management: Shared context requires careful design

⚠️ Integration

Hybrid Approach: Need to maintain both Foundry and Engram systems
Context Translation: Convert between Engram and Foundry contexts
Tool Compatibility: Ensure tools work in both systems

⚠️ Performance

Latency: Multi-agent workflows may take longer
Cost: Multiple agent calls increase token usage
Optimization: Need to optimize workflow execution

Success Metrics

Workflow Execution

✅ Workflow completion rate > 95%
✅ Average workflow duration < 2 minutes
✅ Handoff success rate > 98%

User Experience

✅ User satisfaction with complex workflows
✅ Reduction in manual agent switches
✅ Time saved on multi-step tasks

Quality

✅ Response quality improvement
✅ Comprehensive analysis coverage
✅ Error rate < 2%

Next Steps

Immediate (This Week)

✅ Complete research (this document)
🔄 Review with team
🔄 Finalize workflow designs

Short Term (Next 2 Weeks)

Create Foundry workflow definitions
Test basic workflow execution
Design integration architecture

Medium Term (Next Month)

Implement Foundry orchestrator
Integrate with Engram router
Test with real use cases
Refine based on feedback

References

Last Updated: January 2026

Multi-Agent Orchestration Research - Foundry Integration

Executive Summary

What is Multi-Agent Orchestration?

Definition

Industry Examples

1. IBM watsonx Orchestrate

2. Temporal Multi-Agent Workflows

3. AgentOrchestrationLayer

Current Engram State

Existing Multi-Agent System

Current Orchestration Approach

Azure AI Foundry Orchestration Capabilities

Foundry Multi-Agent Features

1. Workflow Definition

2. Automatic Coordination

3. Workflow Visualization

4. Fault Tolerance

Proposed Integration Approach

Hybrid Strategy

Architecture

Use Cases

1. Requirements → Project Planning

2. Comprehensive Project Analysis

3. Multi-Step Feature Development

Implementation Plan

Phase 1: Research & Design (Week 1)

Phase 2: Foundry Workflow Creation (Week 2)

Phase 3: Integration with Engram (Week 3)

Phase 4: Testing & Refinement (Week 4)

Benefits

✅ Enhanced Capabilities

✅ Improved User Experience

✅ Operational Benefits

Challenges & Considerations

⚠️ Complexity

⚠️ Integration

⚠️ Performance

Success Metrics

Workflow Execution

User Experience

Quality

Next Steps

Immediate (This Week)

Short Term (Next 2 Weeks)

Medium Term (Next Month)

Related Documentation

References