Multi-Agent Orchestration

Multi-agent orchestration coordinates multiple specialized agents working in parallel or in sequence toward a shared goal — combining their diverse capabilities while managing communication, task delegation, and conflict resolution between them.

Why Does This Exist?

A single agent with a large context window and many tools can handle complex tasks, but it has limits: the context fills up, tool calls become slow, and mixing many responsibilities in one agent produces confused behavior. Multi-agent systems solve this with specialization and parallelism: a researcher agent focuses on information gathering, a writer agent focuses on synthesis, a critic agent reviews the output — each doing its one job well, orchestrated by a supervisor.

The result can solve tasks that are too large, complex, or diverse for any single agent.

Think of It Like This

A production team making a documentary

A solo filmmaker can make a short film. To make a feature documentary, you need a director (orchestrator), a research team (research agents), a camera crew (data collection agents), an editor (synthesis agent), and a fact-checker (critic agent). Each specialist is better at their role than any generalist, and they work in parallel. The director doesn't do the camera work — they coordinate the whole production. Multi-agent orchestration is the director's role.

How It Actually Works

The supervisor-worker pattern

The most common multi-agent pattern:

Supervisor agent: Receives the goal, decomposes it into sub-tasks, delegates each to a worker agent, collects results, and synthesizes the final output.

Worker agents: Each specialized for a specific domain or capability:

Research worker: Web search and document retrieval
Code worker: Python execution and debugging
Analysis worker: Statistical analysis and data processing
Writer worker: Text synthesis and formatting

Workers report results back to the supervisor, which decides whether to accept, retry, or re-delegate.

Parallel execution

For tasks with independent sub-goals, workers execute simultaneously:

1Supervisor: "Research these 5 topics"2  → Worker 1: Topic A (running)3  → Worker 2: Topic B (running)4  → Worker 3: Topic C (running)5  → Worker 4: Topic D (running)6  → Worker 5: Topic E (running)7Supervisor: [collect results] → synthesize

This reduces total wall-clock time from $O(n)$ to $O(1)$ (bounded by the slowest worker) for n independent tasks.

Communication patterns

Pattern	Description	Use case
Hub-and-spoke	All agents communicate through a central supervisor	Simple task routing
Peer-to-peer	Agents communicate directly with each other	Debate, negotiation
Blackboard	Agents read/write shared state	Collaborative document editing
Pipeline	Output of agent A is input to agent B	Sequential transformation

Conflict resolution

When workers return conflicting results (different sources give different facts):

Voting: majority wins (works when agents are independent)
Confidence weighting: weight results by agent-reported confidence
Critic agent: a dedicated agent evaluates competing results and picks or reconciles them
Human escalation: defer to a human reviewer for high-stakes conflicts

Code

1# Multi-agent system with LangGraph (graph-based orchestration)2from langchain_openai import ChatOpenAI3from langchain_core.messages import HumanMessage, SystemMessage4from langgraph.graph import Graph, END5from typing import TypedDict, Annotated6import operator7
8llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)9
10# ── Agent state ────────────────────────────────────────────────────────────────11class AgentState(TypedDict):12    goal: str13    research_results: list[str]14    analysis: str15    final_report: str16    messages: Annotated[list, operator.add]17
18# ── Worker agents ─────────────────────────────────────────────────────────────19def research_agent(state: AgentState) -> AgentState:20    """Research worker: gather information on the goal."""21    response = llm.invoke([22        SystemMessage(content="You are a research specialist. Find key facts."),23        HumanMessage(content=f"Research this topic: {state['goal']}"),24    ])25    results = [response.content]26    return {**state, "research_results": results,27            "messages": [("research_agent", response.content)]}28
29
30def analysis_agent(state: AgentState) -> AgentState:31    """Analysis worker: analyze the research findings."""32    research_text = "\n".join(state.get("research_results", []))33    response = llm.invoke([34        SystemMessage(content="You are an analytical specialist. Extract key insights."),35        HumanMessage(content=f"Analyze these findings:\n{research_text}"),36    ])37    return {**state, "analysis": response.content,38            "messages": [("analysis_agent", response.content)]}39
40
41def writer_agent(state: AgentState) -> AgentState:42    """Writer worker: produce the final report."""43    response = llm.invoke([44        SystemMessage(content="You are a technical writer. Produce clear, structured reports."),45        HumanMessage(content=f"Write a report on: {state['goal']}\n\nAnalysis:\n{state['analysis']}"),46    ])47    return {**state, "final_report": response.content,48            "messages": [("writer_agent", response.content)]}49
50
51def supervisor(state: AgentState) -> str:52    """Supervisor decides next step based on current state."""53    if not state.get("research_results"):54        return "research"55    elif not state.get("analysis"):56        return "analyze"57    elif not state.get("final_report"):58        return "write"59    else:60        return END61
62
63# ── Build the graph ────────────────────────────────────────────────────────────64workflow = Graph()65workflow.add_node("research", research_agent)66workflow.add_node("analyze",  analysis_agent)67workflow.add_node("write",    writer_agent)68
69workflow.set_conditional_entry_point(supervisor)70workflow.add_conditional_edges("research", supervisor)71workflow.add_conditional_edges("analyze",  supervisor)72workflow.add_conditional_edges("write",    supervisor)73
74app = workflow.compile()75
76# Run the multi-agent pipeline77result = app.invoke({78    "goal": "Summarize the key benefits of vector databases for ML systems",79    "research_results": [],80    "analysis": "",81    "final_report": "",82    "messages": [],83})84
85print("Final Report:")86print(result["final_report"])87print(f"\nSteps taken: {len(result['messages'])}")

Watch Out For

Cascading failures and error propagation

When a worker agent fails, the supervisor must decide whether to retry, skip, or abort. If failure detection is missing, the supervisor may pass incomplete or wrong results to the next agent — errors propagate and compound. Implement explicit success/failure status in every agent's output, and build retry logic with exponential backoff and fallback strategies.

Context window explosion at the supervisor

If the supervisor receives the full output of every worker for a long task, its context window fills rapidly. Implement summarization at each worker: workers return structured summaries, not raw outputs. Only escalate raw details to the supervisor when there's ambiguity or conflict requiring human-readable context.

The Quick Version

Multi-agent systems coordinate specialized agents — each expert in its domain — through a supervisor.
The supervisor-worker pattern: supervisor decomposes goals, delegates to workers, synthesizes results.
Parallel execution of independent sub-tasks reduces wall-clock time from O(n) to O(1).
Communication patterns: hub-and-spoke, peer-to-peer, blackboard, pipeline.
Key failure modes: cascading errors, context bloat, and missing conflict resolution.