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

# Introduction to orchestration

AI Agent orchestration is the process of coordinating multiple specialized AI agents to work together on complex tasks. It enables you to move beyond single-purpose bots and build sophisticated, multi-step workflows. Lyzr Studio offers two distinct orchestration models for different needs: Workflows and the Manager Agent.

## Workflow

A Workflow is a static, developer-defined blueprint. It's perfect for predictable, repeatable processes where you define every step in advance.

## Manager Agent

The Manager Agent is a dynamic, AI-led orchestrator. It intelligently breaks down a broad goal into a series of sub-tasks and delegates them at runtime.

The choice between them comes down to a simple trade-off: use a DAG for maximum control and efficiency in structured pipelines, or use a Manager Agent for maximum flexibility and intelligence in dynamic, goal-driven tasks.

## Workflow vs Manager Agent

Understanding the distinction between **DAG-based Orchestration** and the **Manager Agent architecture** is essential for building effective and scalable workflows in Lyzr.

While both approaches help structure task execution and coordination, they serve different use cases and follow different design philosophies.

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## 🧭 Core Philosophy

| Feature              | Workflow                          | Manager Agent                              |
| -------------------- | --------------------------------- | ------------------------------------------ |
| **Architecture**     | Static and declarative            | Dynamic and interpretive                   |
| **Definition Style** | Predefined flow using JSON        | AI-led decision making at runtime          |
| **Task Handling**    | Tasks execute in a fixed sequence | Tasks are decomposed and routed on-the-fly |

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## 🔧 Use Case Suitability

| When to Use…             | Workflow                                       | Manager Agent                                    |
| ------------------------ | ---------------------------------------------- | ------------------------------------------------ |
| **Structured Pipelines** | Yes – Perfect for ETL, sequential agent chains | Less Ideal – Overhead for static pipelines       |
| **Dynamic Decisions**    | Limited – Requires manual branching logic      | Yes – AI handles logic and dispatch dynamically  |
| **Error Handling**       | Requires prewritten fallback paths             | Can retry, fallback, or reassign dynamically     |
| **Tool + API Chaining**  | Excellent support                              | Supported via subtasks, but requires good design |
| **Adaptive Workflows**   | Rigid but reliable                             | Highly flexible and goal-driven                  |

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## 🏗️ Technical Comparison

| Feature                  | Workflow                                     | Manager Agent Architecture                      |
| ------------------------ | -------------------------------------------- | ----------------------------------------------- |
| **Input Format**         | JSON config (`tasks`, `default_inputs`)      | JSON + natural language goals                   |
| **Execution Engine**     | LAO (Lyzr Agent Orchestration)               | Orchestrated through a root agent               |
| **Parallel Execution**   | Supported via branches                       | Not parallel by default – sequential delegation |
| **Multi-Agent Handling** | Chained via JSON                             | Native — manager calls multiple worker agents   |
| **Reusability**          | High (via default\_inputs and modular tasks) | Medium — logic tightly embedded in agent flow   |
| **Token Usage**          | More efficient (fewer LLM calls)             | May consume more credits due to LLM reasoning   |
| **Debuggability**        | Easier – predictable task paths              | Harder – runtime dispatch may vary              |

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## 🎯 Decision Guidance

| If you want to…                                         | Use This          |
| ------------------------------------------------------- | ----------------- |
| Build predictable, repeatable multi-step automations    | **Workflow**      |
| Let the agent break down complex tasks into subtasks    | **Manager Agent** |
| Integrate multiple external APIs and agents in sequence | **Workflow**      |
| Build a generalist agent that adapts and delegates      | **Manager Agent** |
| Monitor execution step-by-step with visual logs         | **Workflow**      |
| Delegate tasks to agents based on smart goals           | **Manager Agent** |

***

## 🧠 Summary

| Feature Area    | DAG                   | Manager Agent              |
| --------------- | --------------------- | -------------------------- |
| **Control**     | Developer-defined     | AI-controlled              |
| **Flexibility** | Low–Medium            | High                       |
| **Best For**    | Automation, pipelines | Dynamic task decomposition |
| **Example**     | Lead scoring flow     | Executive assistant agent  |

***

By choosing the right model — **Workflow for structure**, or **Manager Agent for intelligence** — you’ll make your Lyzr workflows both powerful and purpose-built.