Agentic Design Pattern & Multi-Agent Collaboration

Agentic Design Patterns series, we explored foundational concepts around autonomous agents, memory, tools, and reflection. In this part, we dive into Multi-Agent Collaboration — a crucial step towards building distributed, scalable, and context-aware agent ecosystems.

What is Multi-Agent Collaboration?

Multi-agent collaboration refers to a system design where two or more autonomous agents work together to accomplish a shared goal. Instead of one monolithic agent doing everything, tasks are distributed across specialized agents, each with its own capabilities, roles, and responsibilities.

This enables:

• Modularity: Agents can be reused across different systems.
• Parallelization: Tasks can be executed simultaneously.
• Specialization: Each agent focuses on what it does best (e.g., vision, reasoning, planning).

Key Components of a Multi-Agent System

1. Coordinator Agent
   Acts as the orchestrator, breaking tasks into subtasks, delegating them to appropriate agents, and integrating results.

2. Specialist Agents
   Autonomous units focused on specific capabilities like:

   • Text summarization
   • Knowledge retrieval
   • Code analysis
   • Data visualization

3. Shared Memory or Communication Bus
   Allows agents to exchange messages, share progress, or synchronize intermediate outputs.

4. Protocols and Roles
   Agents operate based on agreed-upon protocols (e.g., JSON schemas, APIs, task contracts) to reduce miscommunication.

Pattern: Collaborative Workflow

graph TD;
    User -->|Request| Coordinator
    Coordinator -->|Subtask A| Agent_A
    Coordinator -->|Subtask B| Agent_B
    Agent_A -->|Result A| Coordinator
    Agent_B -->|Result B| Coordinator
    Coordinator -->|Final Output| User

Example Scenario: Document Review

Let’s say a user uploads a legal contract. A multi-agent system could work like this:

• Agent 1: Extracts and summarizes clauses.
• Agent 2: Checks for compliance with legal standards.
• Agent 3: Suggests improvements using case law precedents.
• Coordinator Agent: Aggregates everything into a structured summary for the user.

Collaboration Strategies

1. Task-Based Delegation

The coordinator agent determines tasks based on context and routes them accordingly.

2. Roundtable Discussion

Agents are all aware of each other and pass messages in a loop until consensus is reached or a solution is formed.

3. Dynamic Role Assignment

Agents are selected dynamically based on skills, system load, or trustworthiness.

Tooling and Frameworks

Modern agent frameworks support multi-agent setups:

• LangGraph: For graph-based flow control
• CrewAI: Task delegation and role definition
• Autogen by Microsoft: Multi-agent conversations and tool integration

# Sample pseudocode using CrewAI-like syntax
coordinator.assign([
  {"agent": "summarizer", "task": "Summarize sections"},
  {"agent": "validator", "task": "Check compliance"},
])

Best Challenges to Consider

• Conflict resolution: What if two agents give contradictory results?
• Scalability: How do you coordinate a growing number of agents efficiently?
• Trust and provenance: How do you track which agent produced which output, and why?

Looking Ahead

Like the design pattern of Planning, I find the output quality of multi-agent collaboration hard to predict, especially when allowing agents to interact freely and providing them with multiple tools. The more mature patterns of Reflection and Tool Use are more reliable.

I hope you enjoy playing with these agentic design patterns and that they produce amazing results for you!

If you're interested in learning more, I recommend:

• “Communicative Agents for Software Development,” Qian et al. (2023)

🧠 Curious how to implement your own agentic workflows? Follow me on GitHub or LinkedIn for examples and open-source templates.