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.

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.

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.

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.

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.

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.

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"

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"})

• 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

• Microsoft - AI Agent Design Patterns
• Multi-Agent Orchestration Patterns and ROI
• Enterprise Multi-Agent Orchestration Guide

• 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