Multi-Agent Systems

Multi-Agent Topologies

  1. SEQUENTIAL PIPELINE            2. HIERARCHICAL SUPERVISOR           3. PEER-TO-PEER NETWORK
  [Researcher] ──► [Coder] ──► [QA]          [ MANAGER AGENT ]           [Agent A] ◄──► [Agent B]
                                             /       │       \               ▲              ▲
                                            ▼        ▼        ▼              │              │
                                        [Search]  [Coder]   [QA]         [Agent C] ◄──► [Agent D]

• Sequential Pipeline: Outputs of Agent A feed directly into Agent B (e.g. Research $\to$ Write $\to$ Translate).
• Hierarchical Supervisor: Central Manager agent delegates sub-tasks, collects results, and routes next steps (LangGraph pattern).
• Peer-to-Peer Network: Agents communicate dynamically over a shared message bus (AutoGen pattern).

Role Specialization Pattern

Instead of one giant prompt: "Write, test, and document a Python web scraper":

# Defined via CrewAI / AutoGen
researcher = Agent(role="Web Researcher", tools=[search_tool], goal="Gather API endpoints")
coder      = Agent(role="Python Engineer", tools=[code_executor], goal="Write clean scraper")
reviewer   = Agent(role="Security Auditor", tools=[], goal="Audit code for security flaws")

# Execution flow: Researcher -> Coder -> Reviewer -> Coder (Fixes) -> Final Output

State Management & Shared Memory

Agents communicate by appending messages to a shared State Graph:

$$\text{State}_{t+1} = \text{State}_t \cup { \text{Agent_Name}: \text{Output} }$$

In frameworks like LangGraph, state transitions are modeled as a State Diagram (DAG) with conditional routing edges:

[Start] ──► [Coder Agent] ──► [Test Evaluator] ──(Pass)──► [End]
                                     │
                                   (Fail)
                                     ▼
                              [Debugger Agent] ──► [Coder Agent]

Say this out loud

"Multi-agent systems decompose complex tasks across specialized LLM roles (Researcher, Coder, Reviewer). Orchestration topologies range from Sequential Pipelines to Hierarchical Supervisors that route sub-tasks dynamically. Frameworks like LangGraph use state graphs with conditional edges to manage multi-agent loops, requiring strict iteration bounds to prevent infinite echo loops."

Follow-ups to expect

• What is Human-in-the-Loop (HITL) in Multi-Agent workflows? Interrupting the agent state graph before executing critical nodes (e.g. sending emails or deploying code), prompting human operators to approve, edit, or reject the proposed action.
• How do you prevent context window overflow in long multi-agent sessions? Apply Agent State Summarization: periodically condense historical agent chat turns into a high-level summary string, dropping raw intermediate tool outputs.