> ## Documentation Index
> Fetch the complete documentation index at: https://genai.codewithsiva.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Multi-Agent Orchestration

> Understand multi-agent communication patterns, separation of concerns, and agentic frameworks

Single agents can struggle when tasked with highly complex processes containing multiple distinct steps. **Multi-Agent Orchestration** solves this by breaking a system down into several specialist agents (e.g. researcher, writer, code auditor) that collaborate to achieve a goal.

## 1. Why Use Multi-Agent Collaboration?

Instead of relying on one agent with 20 tools, separating duties yields major benefits:

* **Separation of Concerns**: Each agent has a focused system prompt instruction and role-specific tools, reducing the reasoning burden on the model.
* **Context Preservation**: A single agent loop gains massive context length as it runs multiple tools. Multi-agent systems pass only relevant summaries between nodes, keeping the context window small and cheap.
* **Specialist Personas**: You can use different LLMs for different roles (e.g. a small, fast model for searching, and a large, reasoning model for coding).

## 2. Multi-Agent Design Patterns

Collaborative structures fall into three primary communication patterns:

```text theme={null}
1. Sequential (Pipeline)
[ Researcher Agent ] ──> [ Writer Agent ] ──> [ Editor Agent ] ──> Final Output

2. Hierarchical (Manager-Worker)
                     ┌── [ Specialist Worker A ]
[ Manager Agent ] ───┼── [ Specialist Worker B ]
                     └── [ Specialist Worker C ]

3. Network (Dynamic Conversation)
[ Agent A ] <───(Group Chat Exchange)───> [ Agent B ]
     ▲                                         ▲
     └─────────────────────────────────────────┘
```

### 2.1 Sequential Chains (Pipelines)

The task passes forward through a series of agents. Each agent acts as a filter or refinement step.

* *Example*: A *Researcher* agent extracts web data, passes it to a *Writer* agent to draft a blog post, which passes it to an *Editor* agent for grammar checking.

### 2.2 Hierarchical Orchestration

A central **Supervisor / Manager** agent evaluates the input query and delegates work to specialist child agents, collects their observations, and determines when the overall task is finished.

### 2.3 Network / Dynamic Collaboration

Agents join a shared conversational thread (Group Chat). The next speaker is determined dynamically based on the current context or a pre-defined conversation coordinator.

## 3. Major Multi-Agent Frameworks

To implement these patterns in production, developers use specialized orchestration libraries:

* **CrewAI**: A framework built around structured roles, goals, and tasks. Ideal for setting up role-playing agent "crews" that execute sequential workflows.
* **LangGraph**: An open-source graph orchestrator by LangChain. It offers maximum flexibility to define complex, stateful loops and cyclic agent interactions.
* **AutoGen**: A framework by Microsoft focusing on building conversational multi-agent communication channels.

## 4. Practice Exercises

### Practice 1: Multi-Agent Role Definition

Design a multi-agent team to handle customer refund complaints. Define:

1. The roles needed.
2. The specific tools assigned to each role.
3. The communication sequence.

<Accordion title="Solution">
  #### Role Definition:

  1. **Auditor Agent**:
     * *Role*: Verifies the user's order history and refund eligibility.
     * *Tools*: `query_database`, `check_refund_policy`.
  2. **Support Writer Agent**:
     * *Role*: Writes a professional email explaining the decision.
     * *Tools*: None (requires reasoning only).
  3. **Execution Agent**:
     * *Role*: Processes the financial refund transaction and emails the user.
     * *Tools*: `execute_refund_payment`, `send_email`.

  #### Communication Sequence:

  * **Auditor** analyzes customer ticket $\rightarrow$ passes verification outcome to **Support Writer** $\rightarrow$ **Support Writer** drafts confirmation email $\rightarrow$ **Execution Agent** processes payment and sends email.
</Accordion>