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.
🧭 Core Philosophy
| Feature | DAG | 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 |
🔧 Use Case Suitability
| When to Use… | DAG | 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 |
🏗️ Technical Comparison
| Feature | DAG 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 |
🎯 Decision Guidance
| If you want to… | Use This |
|---|
| Build predictable, repeatable multi-step automations | DAG |
| Let the agent break down complex tasks into subtasks | Manager Agent |
| Integrate multiple external APIs and agents in sequence | DAG |
| Build a generalist agent that adapts and delegates | Manager Agent |
| Monitor execution step-by-step with visual logs | DAG |
| 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 — DAG for structure, or Manager Agent for intelligence — you’ll make your Lyzr workflows both powerful and purpose-built.
