How to Coordinate AI Agents with Multi-Agent Orchestration Patterns

What Are Multi-Agent Orchestration Patterns?

Multi-agent orchestration patterns are architectural approaches for coordinating multiple AI agents to accomplish complex tasks. Rather than relying on a single all-purpose agent, these patterns distribute work across specialized agents that communicate, share state, and hand off tasks in defined ways. The four foundational patterns are:

• Supervisor pattern: A central coordinator assigns tasks and aggregates results
• Pipeline pattern: Agents process work in sequence, each refining the previous output
• Swarm pattern: Autonomous agents work in parallel with minimal coordination
• Hierarchical pattern: Multi-level structure with managers directing specialists

These patterns address the core challenge of multi-agent systems: how do agents share context, avoid conflicts, and produce coherent outputs? The answer varies by use case, but all patterns require some mechanism for shared state.

The Supervisor Pattern

The supervisor pattern uses a single orchestrator agent that delegates tasks to worker agents and synthesizes their outputs. Think of it as a project manager directing a team.

How it works:

1. User request arrives at the supervisor agent
2. Supervisor breaks the task into subtasks
3. Supervisor assigns subtasks to appropriate specialist agents
4. Worker agents complete tasks and return results
5. Supervisor aggregates results and responds to user

When to use it:

• Tasks requiring multiple specialized skills (research, writing, coding)
• Situations needing quality control over agent outputs
• Workflows where a human-like decision point improves results

Trade-offs:

The supervisor becomes a bottleneck. Every interaction routes through it, adding latency. The supervisor also needs enough context to make good delegation decisions, which can strain token limits in complex workflows.

Example implementation:

class SupervisorAgent:
    def __init__(self, workers: list[Agent]):
        self.workers = {w.name: w for w in workers}
        self.storage = FastIOWorkspace("supervisor-context")

    def process(self, task: str) -> str:
        ### Break task into subtasks
        plan = self.plan_subtasks(task)

        ### Delegate to workers
        results = []
        for subtask in plan:
            worker = self.workers[subtask.agent_name]
            result = worker.execute(subtask.instructions)
            ### Store intermediate results for context
            self.storage.save(f"{subtask.id}.json", result)
            results.append(result)

        ### Synthesize final response
        return self.synthesize(results)

The supervisor pattern dominates enterprise deployments because it mirrors human organizational structures. Gartner reported a 1,445% increase in multi-agent system inquiries from Q1 2024 to Q2 2025, with supervisor-based architectures driving most production implementations.

The Pipeline Pattern

The pipeline pattern chains agents in sequence, where each agent processes the output of the previous one. This mirrors assembly lines or content production workflows.

How it works:

1. Input enters the first agent in the pipeline
2. Each agent transforms, enriches, or refines the data
3. Output flows to the next agent automatically
4. Final agent produces the completed result

When to use it:

• Content production (research → write → edit → format)
• Data processing with clear transformation stages
• Quality control workflows where each stage adds value
• Tasks with natural sequential dependencies

Pipeline patterns appear in 60% of production multi-agent systems because they match how work naturally flows. The pattern works best when each stage has a clear input/output contract.

Example: Content Pipeline

Researcher → Writer → Editor → Humanizer
    ↓           ↓         ↓          ↓
 Sources    Draft    Polished    Final

Trade-offs:

Pipeline failures cascade. If stage 3 produces poor output, stage 4 cannot recover. You need checkpoints and rollback mechanisms. Latency compounds since each stage must complete before the next begins.

Storage requirement:

Pipelines need persistent storage between stages. Each agent must read the previous stage's output and write its own. Without shared storage, you end up passing massive payloads between agents, hitting context limits and losing intermediate work if any stage fails. ```python class PipelineStage: def init(self, workspace: str): self.storage = FastIOWorkspace(workspace)

def execute(self, input_key: str, output_key: str):
    ### Read previous stage output
    input_data = self.storage.read(input_key)

    ### Process
    result = self.process(input_data)

    ### Write for next stage
    self.storage.save(output_key, result)
    return output_key

The Swarm Pattern

The swarm pattern deploys multiple agents working in parallel on related tasks, with minimal central coordination. Agents operate autonomously and may compete or collaborate.

How it works:

1. Multiple agents receive the same or related tasks
2. Agents work independently and simultaneously
3. Results are aggregated, voted on, or merged
4. Best output wins or outputs combine

When to use it:

• Tasks benefiting from diverse approaches (brainstorming, exploration)
• Fault tolerance requirements (if one agent fails, others continue)
• Speed-critical workloads (parallel execution beats sequential)
• Research and analysis with multiple valid perspectives

Trade-offs:

Resource intensive. Running five agents costs five times the compute of one agent. Coordination overhead emerges when combining outputs. You may get contradictory results that need reconciliation.

Conflict resolution:

Swarms must handle agents modifying the same resources. Options include:

• Voting: Majority result wins
• Scoring: Quality metric determines winner
• Merge: Combine non-conflicting elements
• Human review: Flag conflicts for manual resolution

Example: Research Swarm

async def research_swarm(query: str, num_agents: int = 3):
    workspace = FastIOWorkspace("research-swarm")

    ### Launch agents in parallel
    tasks = [
        research_agent(query, f"agent-{i}", workspace)
        for i in range(num_agents)
    ]
    results = await asyncio.gather(*tasks)

    ### Each agent saved findings to shared storage
    all_findings = workspace.list_files("findings/")

    ### Synthesize diverse perspectives
    return synthesize_findings(all_findings)

The Hierarchical Pattern

The hierarchical pattern creates multiple levels of agent management, with executive agents directing manager agents who direct worker agents. This scales the supervisor pattern for complex organizations.

How it works:

1. Executive agent receives high-level objectives
2. Executive breaks objectives into projects for manager agents
3. Manager agents decompose projects into tasks for workers
4. Workers execute and report up the chain
5. Results aggregate upward through the hierarchy

When to use it:

• Large-scale systems with many specialized agents
• Complex domains requiring different expertise levels
• Organizations wanting AI to mirror their structure
• Workflows requiring approval chains or escalation

Example structure:

CEO Agent
    ├── Engineering Manager Agent
    │       ├── Backend Developer Agent
    │       ├── Frontend Developer Agent
    │       └── QA Agent
    └── Content Manager Agent
            ├── Writer Agent
            ├── Editor Agent
            └── SEO Agent

Trade-offs:

Hierarchies add latency at each level. Communication overhead grows with depth. You need clear delegation rules to prevent agents from escalating everything upward. The pattern can become rigid, handling novel situations poorly.

State management challenge:

Hierarchical systems generate state at every level. Each manager needs visibility into their workers' progress. Each executive needs summaries from managers. Without persistent shared storage, this context constantly re-transmits through the hierarchy, wasting tokens and risking inconsistency. Proper orchestration reduces agent conflicts by 80% when using hierarchical patterns with shared state management.

Choosing the Right Pattern

No single pattern fits all use cases. Here's a decision framework:

Pattern combinations:

Real systems often combine patterns. A hierarchical structure might use pipelines within each team. A supervisor might spawn swarms for parallel exploration. The key is matching pattern to task characteristics:

• Sequential dependencies → Pipeline
• Need for oversight → Supervisor or Hierarchical
• Parallelizable work → Swarm
• Scale requirements → Hierarchical

Questions to ask:

1. Do tasks depend on each other's outputs? (Yes → Pipeline)
2. Do I need quality control checkpoints? (Yes → Supervisor)
3. Can work happen in parallel? (Yes → Swarm)
4. Am I coordinating more than 10 agents? (Yes → Hierarchical)

The Shared Storage Problem

Every orchestration pattern shares one requirement: agents need persistent, shared storage for coordination. Without it, you face:

• Context loss: Intermediate results disappear between agent calls
• Payload bloat: Passing full context through every handoff
• Race conditions: Agents overwriting each other's work
• Recovery failures: No checkpoint to resume from after crashes

Most documentation describes patterns abstractly without addressing how agents actually share files and state. This gap explains why many multi-agent POCs fail when moving to production.

What agents need from storage:

• Persistent workspaces: Files survive agent restarts
• Concurrent access: Multiple agents read/write safely
• Permissions: Control which agents access which files
• Versioning: Track changes and enable rollback
• API access: Programmatic operations, not manual uploads

The Fast.io approach:

AI agents register for storage accounts like human users. Each agent gets:

• Personal workspace for private working files
• Shared workspaces for team coordination
• Full API access for file operations
• MCP integration for Claude and compatible agents
• Free tier with 5,000 credits monthly

from fastio import Agent

### Agent authenticates like a user
agent = Agent.authenticate(api_key="...")

### Create workspace for this pipeline run
workspace = agent.create_workspace("content-pipeline-2024-02")

### Upload files, share with other agents
workspace.upload("research.json", research_data)
workspace.invite("writer-agent@fastio.ai", role="editor")

The MCP server at mcp.fast.io provides native integration for Claude-based agents, eliminating boilerplate for common file operations.

Implementation Checklist

When building multi-agent systems, address these requirements:

Architecture decisions

• Choose primary orchestration pattern based on workflow shape
• Define agent boundaries and responsibilities
• Establish handoff protocols between agents
• Plan for hybrid patterns if needed

State management

• Set up persistent storage accessible to all agents
• Define file naming conventions for clarity
• Implement conflict resolution for concurrent writes
• Create rollback/recovery procedures

Agent configuration

• Provision storage accounts for each agent
• Configure workspace permissions appropriately
• Set up API keys and authentication
• Test agent-to-agent file sharing

Monitoring

• Log agent activities and handoffs
• Track pipeline stage completion times
• Monitor storage usage and quotas
• Alert on failures and retries

Testing

• Unit test individual agents
• Integration test full pipelines
• Load test concurrent agent operations
• Chaos test recovery procedures