multi-agent-orchestration

name	Multi-Agent Orchestration
description	Workflow patterns for multi-agent systems across frameworks. Directs to RAG for implementation.

Multi-Agent Orchestration Workflow

Pattern Selection

Pattern	When to Use	Complexity	RAG Query
Delegation	Route to specialists by task type	Low	"agent delegation routing"
Sequential	Pipeline with handoffs	Low	"sequential agent pipeline"
Parallel	Independent concurrent tasks	Medium	"parallel agent execution"
Hierarchical	Manager coordinates workers	Medium	"hierarchical agent manager"
Mesh	Peer-to-peer collaboration	High	"agent mesh communication"

Framework-Specific Patterns

ADK Multi-Agent

Uses sub_agents for delegation. RAG Query: mcp__agentic-rag__query_sdk("sub_agents delegation", sdk="adk", mode="build")

Key: Agent description drives routing decisions.

LangGraph Multi-Agent

Uses graph nodes for each agent, conditional edges for routing. RAG Query: mcp__agentic-rag__query_sdk("multi-agent graph", sdk="langgraph", mode="build")

Key: State must flow between agent nodes.

CrewAI Multi-Agent

Uses Crew with agents and tasks. RAG Query: mcp__agentic-rag__query_sdk("crew agents tasks", sdk="crewai", mode="build")

Key: Process type (sequential vs hierarchical) changes everything.

OpenAI Multi-Agent

Uses handoffs between agents. RAG Query: mcp__agentic-rag__query_sdk("agent handoff", sdk="openai", mode="build")

Key: Handoff descriptions determine routing.

Routing Strategy Decision

Strategy	Best For	Tradeoff	RAG Query
Keyword	Simple, predictable tasks	Brittle	"keyword routing"
LLM-based	Complex, nuanced routing	Slower, costlier	"llm routing classification"
Semantic	Similar intent detection	Needs embeddings	"semantic routing embedding"
Rule-based	Deterministic, auditable	Manual maintenance	"rule based routing"

Critical Gotchas

These cause multi-agent failures:

1. Vague descriptions - Agents won't be routed correctly if descriptions are generic
2. Missing state passing - Context lost between agents
3. Circular delegation - Agent A → B → A infinite loop
4. No termination condition - Loops forever
5. Sequential when could parallel - Unnecessary latency
6. No error handling - One agent failure crashes system

State Sharing Patterns

Pattern	When to Use	RAG Query
Shared dict/TypedDict	Simple, synchronous	"shared state dict"
Message history	Conversational	"message passing agents"
External store	Persistence needed	"agent state persistence"
Event bus	Decoupled agents	"event driven agents"

Workflow: Building Multi-Agent System

Step 1: Define Agent Responsibilities

Each agent needs:

• Clear, specific role
• Well-defined capabilities
• Distinct from other agents

Step 2: Choose Orchestration Pattern

Use table above to select pattern based on your workflow.

Step 3: Design Routing Logic

RAG Query: mcp__agentic-rag__search("[routing strategy] [framework]", mode="build")

Step 4: Implement State Sharing

RAG Query: mcp__agentic-rag__search("state sharing [framework]", mode="build")

Step 5: Add Termination Conditions

• Max iterations
• Goal achievement check
• Timeout

Step 6: Error Handling

• Fallback agents
• Retry logic
• Graceful degradation

Common Error Patterns

Symptom	Likely Cause	Fix
Wrong agent called	Vague description	Make descriptions specific
Infinite loop	No termination	Add max iterations, goal check
Context lost	No state passing	Implement state sharing
Slow response	Sequential unnecessary	Use parallel where possible
One failure crashes all	No error handling	Add try/catch, fallbacks

Performance Optimization

Issue	Solution	RAG Query
Latency	Parallel where possible	"parallel agent optimization"
Token usage	Shorter agent contexts	"agent context optimization"
Routing errors	Better descriptions	"agent description best practices"
State bloat	Prune old messages	"state management cleanup"