====== Agent Orchestration ======

Agent orchestration is the discipline of coordinating one or more AI agents to accomplish complex tasks through structured patterns of communication, delegation, and synthesis. As enterprise AI adoption has grown — with 72% of 2025 enterprise projects using multi-agent systems — orchestration has become the key architectural decision determining system reliability, performance, and cost.

===== Orchestration Patterns =====

==== Single Agent ====

One agent handles the entire task using reasoning, planning, and tool calling in a loop. The agent receives a goal, breaks it into steps, executes tools, and synthesizes results.

**When to use:** Simple, self-contained tasks. Prototyping. When coordination overhead would exceed the task complexity.

**Limitations:** Performance degrades on multi-step tasks requiring diverse expertise. Context window limits constrain task scope.

==== Multi-Agent ====

Multiple specialized agents collaborate on subtasks via decomposition and delegation. Agents may communicate sequentially, in parallel, or peer-to-peer.

**When to use:** Complex workflows requiring diverse expertise. Tasks where specialization improves quality (e.g., one agent for research, another for writing). Enterprise systems report 45% faster resolution and 60% higher accuracy versus single agents.

==== Hierarchical ====

A manager/orchestrator agent decomposes high-level goals into subtasks, delegates to worker agents, monitors progress, and synthesizes outputs. Workers may have their own sub-hierarchies.

**When to use:** Structured delegation with clear authority chains. Team-like coordination. Reduces handoffs by 45% and boosts decision speed 3x compared to flat multi-agent.

==== Pipeline (Sequential) ====

Agents process data in a linear chain where each agent's output feeds the next. Example: extraction agent -> validation agent -> routing agent -> response agent.

**When to use:** Ordered, predictable workflows. Document processing, compliance checks, ETL pipelines. When each step has clear input/output contracts.

==== Map-Reduce ====

A coordinator splits work into parallel subtasks ("map"), specialized agents process each independently, then a reducer agent aggregates results into a final output.

**When to use:** Embarrassingly parallel tasks. Analyzing multiple documents simultaneously. Batch processing, large-scale data analysis, multi-source research.

===== Pattern Selection Guide =====

<code python>
def select_orchestration_pattern(task):
    """Decision framework for choosing an orchestration pattern."""
    if task.is_simple and task.steps < 3:
        return "single_agent"
    elif task.is_parallelizable and task.subtasks_independent:
        return "map_reduce"
    elif task.is_sequential and task.steps_ordered:
        return "pipeline"
    elif task.needs_oversight and task.has_subtask_hierarchy:
        return "hierarchical"
    else:
        return "multi_agent"
</code>

===== Frameworks =====

| **Framework** | **Pattern Strength** | **Key Feature** | **License** |
| [[https://langchain-ai.github.io/langgraph/|LangGraph]] | Hierarchical, Pipeline | Graph-based cyclic stateful flows | MIT |
| [[https://www.crewai.com/|CrewAI]] | Multi-Agent, Hierarchical | Role-based crew orchestration | MIT |
| [[https://microsoft.github.io/autogen/|AutoGen]] | Multi-Agent, Map-Reduce | Conversational group chats, handoffs | MIT |
| [[https://github.com/openai/swarm|OpenAI Swarm]] | Multi-Agent | Lightweight agent handoffs | MIT |
| [[https://cloud.google.com/products/agent-development-kit|Google ADK]] | All patterns | Production agent deployment on Google Cloud | Apache 2.0 |

===== Example: Hierarchical Orchestration =====

<code python>
from langgraph.graph import StateGraph, END

def orchestrator(state):
    task = state["task"]
    subtasks = llm_decompose(task)
    return {"subtasks": subtasks, "results": []}

def research_agent(state):
    result = llm_research(state["current_subtask"])
    state["results"].append(result)
    return state

def synthesis_agent(state):
    final = llm_synthesize(state["results"])
    return {"output": final}

# Build hierarchical graph
graph = StateGraph(dict)
graph.add_node("orchestrator", orchestrator)
graph.add_node("researcher", research_agent)
graph.add_node("synthesizer", synthesis_agent)
graph.add_edge("orchestrator", "researcher")
graph.add_conditional_edges("researcher", should_continue,
    {"continue": "researcher", "done": "synthesizer"})
graph.add_edge("synthesizer", END)
graph.set_entry_point("orchestrator")

app = graph.compile()
result = app.invoke({"task": "Analyze Q4 sales across all regions"})
</code>

===== Communication Protocols =====

  * **A2A (Agent-to-Agent)** — Google's protocol for standardized inter-agent communication
  * **MCP (Model Context Protocol)** — Anthropic's standard for connecting agents to tools and data sources
  * **Direct message passing** — Agents share state through shared memory or message queues
  * **Event-driven** — Agents react to events published to a shared bus

===== References =====

  * [[https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns|Microsoft - AI Agent Design Patterns]]
  * [[https://www.onabout.ai/p/mastering-multi-agent-orchestration-architectures-patterns-roi-benchmarks-for-2025-2026|Multi-Agent Orchestration Patterns and ROI]]
  * [[https://aetherlink.ai/en/blog/ai-agents-multi-agent-orchestration-enterprise-guide-2026|Enterprise Multi-Agent Orchestration Guide]]

===== See Also =====

  * [[agent_frameworks]] — Framework implementations for orchestration
  * [[agent_debugging]] — Observability for multi-agent systems
  * [[agent_safety]] — Safety in orchestrated agent deployments
  * [[function_calling]] — Tool calling that agents use within orchestration