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Module 08 · Page 9 of 20 · 9 sections

Multi-Agent Systems in Agentic AI

Teams of specialised agents: architectures, collaboration types, communication and a LangGraph build.

03Types of Agents14Agentic AI Design Patterns and Architectures04Agent Frameworks and Tools
8.1

What are Multi-Agent Systems?

  • Multi-agent systems consist of multiple agents working together to solve complex tasks.
  • Each agent has a specific role, goal and tools.
  • Agents can collaborate, communicate and coordinate to achieve a common objective.
  • Useful for large, real-world tasks where a single agent may not be sufficient.
8.2

Why Use Multi-Agent Systems?

  • Breaks complex tasks into smaller sub-tasks.
  • Improves accuracy and reliability.
  • Different agents can specialize in different skills.
  • Enables parallel execution and better scalability.
  • Useful in real-world applications like research, content creation, data analysis and enterprise workflows.
8.3

Common Architecture

User QueryHigh level goal
Manager AgentPlanner / orchestrator
Research AgentFind information
Web Search Tools
Writer AgentGenerate content
LLM + Tools
Reviewer AgentCheck quality
Validation Tools
Final OutputCombined result
8.4

Types of Multi-Agent Collaboration

1
Hierarchical
One manager agent coordinates sub-agents.
2
Collaborative
Agents work together and share information.
3
Sequential
Agents work in a sequence (one after another).
4
Competitive
Multiple agents provide solutions and the best one is selected.
5
Hybrid
Combination of above approaches.
Related14Agentic AI Design Patterns and Architectures
8.5

Communication Between Agents

  • Agents can share messages, context and intermediate results.
  • Communication can be done using:
    • Shared memory (database / vector store)
    • Message passing (direct agent-to-agent)
    • Central orchestrator (manager agent)
Related07Memory in Agentic AI
8.6

Example: Research Assistant Multi-Agent System

User Query“Create a report on renewable energy trends”
Manager AgentPlan and assign tasks
Research AgentSearch latest data
Web Search API
Analysis AgentSummarize insights
Data Analysis Tools
Writer AgentGenerate report
LLM (e.g. GPT-4)
Final ReportComplete answer with analysis and references
Related09Building Real-World Projects with Agentic AI
8.7

Example Workflow (Step by Step)

  1. 1User provides a high level goal.
  2. 2Manager agent breaks the goal into sub-tasks.
  3. 3Each sub-agent performs its task using appropriate tools.
  4. 4Agents share results with manager or directly with each other.
  5. 5Final output is generated by combining all results.
8.8

Implementation Example (LangGraph)

  • LangGraph allows building stateful multi-agent workflows.
  • Each node in the graph can be an agent with a specific role.
  • Agents can pass messages and maintain shared state.
  • It is useful for complex workflows with loops and conditions.
python
from langgraph.graph import StateGraph

graph = StateGraph()

graph.add_node("research_agent", research_node)
graph.add_node("analysis_agent", analysis_node)
graph.add_node("writer_agent", writer_node)

graph.set_entry_point("research_agent")

graph.add_edge("research_agent", "analysis_agent")
graph.add_edge("analysis_agent", "writer_agent")

graph.set_finish_point("writer_agent")
Related04Agent Frameworks and Tools
8.9

Key Takeaways

  1. 1Multi-agent systems are powerful for complex and real-world tasks.
  2. 2Each agent can have a specific role, goal and set of tools.
  3. 3Agents can collaborate, communicate and share context.
  4. 4Manager agent helps in planning, coordination and final output.
  5. 5Frameworks like LangGraph, CrewAI and AutoGen make it easy to build multi-agent systems.