Building a Basic Agent
Overview
Agents are the core building blocks of AI systems, responsible for executing specific tasks, making decisions, and interacting with users or other agents. A well-designed agent architecture is crucial for achieving optimal performance, scalability, and maintainability in AI applications. This hands-on guide demonstrates how to create a basic agent using Microsoft AutoGen or LangGraph, powerful AI toolkits that simplifies agent development and deployment.
AutoGen focuses on autonomous multi-agent interactions, while LangGraph emphasizes structured, graph-based execution with deterministic workflows. You should be able to determine the right agent pattern and toolkit for your use case and use the right toolkit for AI production
Architecture & Design Philosophy
- AutoGen: Designed as an agent-based framework, where multiple AI agents communicate and collaborate autonomously to solve tasks. It focuses on multi-agent coordination and self-improving workflows.
- LangGraph: Based on a graph-based execution model, allowing for flexible, deterministic flow control in multi-step AI pipelines. It is optimized for directed acyclic graphs (DAGs) to structure agent interactions explicitly.
Execution & Workflow Control
- AutoGen: Uses dynamic interactions between agents, where agents decide their next steps based on the context, making the workflow more flexible but less predictable.
- LangGraph: Employs predefined execution flows using a graph structure, ensuring better control over the sequence of operations while still allowing for dynamic logic.
Use Cases & Flexibility
- AutoGen: Best suited for autonomous multi-agent collaboration, such as AI-driven dialogue, problem-solving, and self-learning agents.
- LangGraph: More suitable for structured, deterministic AI pipelines, such as workflow automation, task-specific retrieval-augmented generation (RAG), and multi-step reasoning.
Step-by-Step Guide
Step 1. Construct and Visualize Agents
Visualizing abstract agents through sketches or diagrams is essential for several reasons:
- Improved Comprehension – Abstract agents and their interactions can be difficult to grasp in purely textual or code-based formats. Visual representations make it easier to understand complex relationships and data flows at a glance.
- Debugging and Troubleshooting – Visualizing agents helps identify inconsistencies, missing connections, or unexpected interactions in a multi-agent system. This proactive approach can reduce debugging time and prevent potential issues.
- Efficient System Design – By mapping out interactions between agents, designers can optimize workflows, remove redundancies, and ensure that the architecture is well-structured before implementation.
AutoGen does not support visualization in a form similar to langgraph. Thus, the author created a custom toolkit (azure_genai_utils), which can also help in building AutoGen-based agents.
Step 2. Implement Agents in AutoGen or LangGraph
If you are not familiar with AutoGen/LangGraph, you can start with the AutoGen Hands-On or LangGraph Hands-On to get a better understanding of the concepts.