The 11 Multi-Agent Orchestration Patterns: Complete Guide

How to structure the collective intelligence of your AI agents

Related articles: AI Agent Design Guide | Building an Agent: The Building Blocks | Meta-Analysis of AI Agent Capabilities

Executive Summary

In brief: Multi-agent orchestration enables multiple AI agents to collaborate on solving complex problems. This guide presents 11 fundamental patterns — from the simple sequential Pipeline to the emergent Swarm — each suited to specific contexts. Patterns differ by their level of control (centralized vs distributed), complexity, and use case: Pipeline for linear workflows, Supervisor for intelligent routing, Fan-out/Fan-in for parallelization, Evaluator/Critic for iterative quality, Council for multi-expert decisions. The key: understand each pattern’s strengths and combine them like Lego bricks.

Multi-agent orchestration is the art of making multiple AI agents collaborate to solve complex problems. But how do you organize this collaboration? There are several patterns, each suited to specific contexts.

Here are the 11 fundamental patterns that every AI architect should master.

1. Pipeline — Sequential Processing

The Concept

The Pipeline pattern organizes agents in a linear chain. Each agent processes the output of the previous one and passes the result to the next. Simple, predictable, efficient.

Diagram

When to Use

• Processes with well-defined sequential steps
• Cascading data transformations
• Workflows where each step depends on the previous one

Practical Case: HR Application Processing

A company receives 500 resumes per day. The pipeline:

1. Extraction Agent → Parses the resume (PDF, Word) into structured data
2. Scoring Agent → Evaluates job/profile fit (0-100)
3. Enrichment Agent → Searches LinkedIn, portfolio, references
4. Writing Agent → Generates a summary sheet for the recruiter

Result: 500 resumes processed in 2 hours instead of 3 days, with standardized sheets.

2. Supervisor — Centralized Delegation

The Concept

A central Supervisor agent receives requests, decides which specialized agent should intervene, delegates the task, then collects and synthesizes the results.

Diagram

When to Use

• Intelligent request routing
• Systems with specialized agents
• Need for centralized coordination

Practical Case: Multi-Brand Customer Support

A hotel group with 3 brands (luxury, business, budget):

• Supervisor → Analyzes customer request, identifies brand and request type
• Booking Agent → Modifications, cancellations, availability
• Concierge Agent → Restaurant recommendations, activities
• Complaint Agent → Dispute management with calibrated empathy
• Billing Agent → Payment questions, invoices, refunds

Result: A single chatbot for all brands, intelligent routing, consistent experience.

3. Collaborative — Free Peer-to-Peer

The Concept

Agents communicate freely with each other without hierarchy. Each can request any other agent according to their needs. Emergent self-organization.

Diagram

When to Use

• Exploratory problems without predefined path
• Teams of agents with complementary skills
• Situations requiring adaptability

Practical Case: Product Innovation Cell

A startup develops a new SaaS product:

• Market Research Agent → Analyzes trends, competition
• UX Agent → Proposes user journeys
• Tech Agent → Evaluates technical feasibility
• Business Agent → Models the business model

Agents collaborate freely, each enriching the work of others until convergence.

4. Hierarchical — Multi-Level Supervision

The Concept

Tree organization with multiple levels of supervision. Managers delegate to sub-managers or worker agents. Scalable and structured.

Diagram

When to Use

• Complex organizations requiring delegation
• Projects with nested sub-projects
• Need for scalability with control

Practical Case: Automated Financial Audit

An accounting firm audits a multinational’s accounts:

• Director → Coordinates the global audit, consolidates conclusions
  • Balance Sheet Manager → Supervises assets/liabilities analysis
    • Fixed Assets Worker
    • Receivables Worker
    • Debts Worker
  • P&L Manager → Supervises income statement analysis
    • Revenue Worker
    • Expenses Worker

Result: Complete audit in parallel, structured anomaly escalation.

5. Fan-out/Fan-in — Parallelization and Aggregation

The Concept

A dispatcher distributes a task to multiple agents in parallel (fan-out). An aggregator collects and synthesizes the results (fan-in).

Diagram

When to Use

• Parallelizable tasks without dependencies
• Multi-source research
• Processing time optimization

Practical Case: Competitive Intelligence

A company monitors 20 competitors:

• Dispatcher → Distributes the list of competitors
• Analyst Agents (×20 in parallel) → Each analyzes a competitor (news, patents, recruitment, social media)
• Aggregator → Synthesizes into unified report with priority alerts

Result: Exhaustive monitoring in 5 minutes instead of 2 days.

6. Evaluator/Critic — Generation + Validation

The Concept

One agent generates an output, another evaluates and critiques it. Iterative loop until a quality threshold is reached.

Diagram

When to Use

• High-quality content generation
• Self-correcting code
• Any output requiring iterative validation

Practical Case: Legal Contract Drafting

A law firm automates NDA drafting:

• Generator → Drafts the contract according to parameters (parties, jurisdiction, duration)
• Evaluator → Checks:
  • Legal compliance (mandatory clauses present)
  • Internal consistency (no contradictions)
  • Balance of obligations (no leonine clauses)
  • Style (correct legal terminology)
• Loop → Iterates until score > 95%

Result: Firm-quality contracts in 3 minutes, with iteration audit trail.

7. Blackboard — Shared State + Controller

The Concept

Agents share a common space (blackboard) where they read and write. A controller orchestrates who intervenes when, based on the blackboard state.

Diagram

When to Use

• Problems requiring progressive knowledge accumulation
• Complex multi-expertise diagnosis
• Situations where intervention order depends on state

Practical Case: Industrial Fault Diagnosis

An automotive factory diagnoses line stoppages:

Blackboard contains:

• Observed symptoms
• Fault hypotheses
• Tests performed
• Real-time sensor data

Specialist Agents:

• Electrical Agent → Analyzes circuits, sensors
• Mechanical Agent → Analyzes wear, vibrations
• Software Agent → Analyzes PLC logs
• Process Agent → Analyzes production parameters

Controller → Activates the relevant agent based on diagnostic state

Result: Expert diagnosis in 10 minutes, capitalization of solved cases.

8. Debate — Adversarial Argumentation

The Concept

Two or more agents confront each other on a question, each defending a position. A judge decides or synthesizes. Useful for exploring pros/cons.

Diagram

When to Use

• Strategic decisions with risks
• Need to explore contradictory arguments
• Reducing confirmation bias

Practical Case: VC Investment Committee

A venture capital fund evaluates a startup:

• Advocate Agent → Defends the investment
  • Market potential, team, traction, differentiation
• Devil’s Advocate Agent → Attacks the investment
  • Market risks, team weaknesses, burn rate, competition
• Judge Agent → Synthesizes, weighs, recommends
  • Investment score + suggested conditions

Result: Balanced due diligence, documented decisions, less enthusiasm bias.

9. Reflection — Self-Critique and Improvement

The Concept

A single agent generates an output, then self-evaluates, identifies its weaknesses, and improves iteratively.

Diagram

When to Use

• Creative tasks requiring refinement
• Complex step-by-step reasoning
• When a single agent suffices but must excel

Practical Case: Strategic Presentation Preparation

A marketing director prepares a board presentation:

Reflection Agent in 4 passes:

1. Draft → Generates initial structure and content
2. Reflect → “Is this presentation convincing? Do the data support the thesis?”
3. Critique → “Slide 4 lacks evidence. The conclusion is weak. Too much jargon.”
4. Improve → Corrects identified weaknesses

Result: Consulting-quality presentation, self-improved in 3 iterations.

10. Swarm — Emergent Collective Intelligence

The Concept

Many simple agents interact locally according to simple rules. Intelligent behavior emerges from these interactions.

Diagram

When to Use

• Massive exploration of solution spaces
• Combinatorial optimization
• Adaptive and resilient systems

Practical Case: Last-Mile Logistics Optimization

An e-commerce company optimizes 500 daily deliveries:

Swarm of micro-agents (1 per package):

• Each agent knows: origin, destination, time constraints
• Local rules: “If a neighbor is going in my direction, propose grouping”
• Digital pheromones: Good routes are reinforced

Emergence: Optimal routes form organically through local aggregation

Result: -23% kilometers traveled, real-time adaptation to disruptions.

11. Council / Ensemble — Multi-Expert Deliberation

The Concept

Multiple expert agents deliberate together on a question. Each brings their perspective, then the group converges toward a collective decision.

Diagram

When to Use

• Complex multi-dimensional decisions
• Need to cross multiple expertises
• Reducing error risk through redundancy

Practical Case: M&A Project Evaluation

An industrial group evaluates an acquisition target:

Expert Council:

• Finance Expert → Valuation, synergies, deal structure
• Legal Expert → Legal due diligence, litigation risks
• Operations Expert → Industrial integration, operational synergies
• HR Expert → Culture fit, key talent retention
• Market Expert → Competitive positioning post-merger

Deliberation: Each expert shares their analysis, others challenge

Consensus: Weighted vote → Go / No-Go / Conditions

Result: Robust decision, minimized blind spots, complete documentation.

Summary Table

How to Choose?

Ask yourself these questions:

1. Is the task sequential or parallelizable?

   • Sequential → Pipeline
   • Parallelizable → Fan-out/Fan-in

2. Do I need centralized control?

   • Yes → Supervisor or Hierarchical
   • No → Collaborative or Swarm

3. Does quality take priority over speed?

   • Yes → Evaluator/Critic or Reflection
   • No → Direct Pipeline

4. Does the problem require multiple expertises?

   • Yes, in sequence → Pipeline
   • Yes, in parallel → Council or Fan-out
   • Yes, with debate → Debate

5. Does the problem state evolve?

   • Yes, shared state needed → Blackboard
   • No → Other patterns

Conclusion

These 11 patterns are not mutually exclusive. The most powerful multi-agent architectures combine multiple patterns:

• A Supervisor that routes to specialized Pipelines
• A Council where each expert uses an internal Evaluator/Critic
• A Blackboard fed by a Swarm of collector agents

The key is understanding each pattern’s strengths and assembling them like Lego bricks to build the architecture suited to your problem.

Multi-agent orchestration is not about technology, it’s about design. Choose the right pattern, and your agents will work together like a cohesive team.

Further Reading

• AI Agent Design Guide: What Works, What Fails — The golden rule of deterministic feedback, decision matrices and design principles
• Meta-Analysis: Capabilities, Limitations and Patterns of AI Agents — Analysis of 100+ publications: viable patterns vs illusions
• Building an Agent: The Art of Assembling the Right Building Blocks — Languages, orchestration frameworks, models and infrastructure
• Agent Skills: The Onboarding Manual — Structuring instructions for specialized agents

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