“Beginner's Guide to building Autonomous AI Agents with Eliza Framework”
The talk
Abstract
AI agents are becoming fundamental building blocks of modern applications, thanks to their capability of understanding context with the help of the LLMs, but with added capability to interact with systems, files. AI agents that can meaningfully interact across platforms while maintaining context aren't just for large tech companies anymore as we have a number of open source AI Agent frameworks now which developers can leverage. This beginner-friendly, hands-on tutorial shows the process of creating autonomo
Prerequisites
basics of python, vector search, llm
Description
We'll break down the complexities of AI agent development into understandable components. Starting with basic concepts and progressing to functional implementations, participants will learn how to create, deploy, and manage AI agents that can handle real-world tasks with the help of the Eliza AI Agent framework. Finally we will have a hands on demo
Key Learning Outcomes:
- Understand the fundamental architecture of AI agents
- Build a basic AI agent using the Eliza framework
- Design agent-to-agent communication patterns
- Deploy agents that can interact across platforms
Tutorial Outline
Part 1: Understanding AI Agents and Setup
- Introduction to AI Agents What are AI agents and why they matter Understanding agent components:
- Memory and context management
- Decision making capabilities
- System interactions Introduction to Eliza framework architecture
Part 2: Building Your First Agent
-
Creating a Basic Agent Basic agent structure walkthrough Setting up agent configuration Implementing first interactions Setting up agent personality and behavior Testing basic responses
-
Adding Memory and System Interaction Why do agents need persistent storage? Setting up Couchbase for agent memory
Managing agent state Handling context in conversations
Part 3: Advanced Features and Deployment
-
Extending Agent Capabilities Adding custom skills Implementing system commands Basic error handling Monitoring agent behavior
-
Production Considerations Deployment best practices Common pitfalls and solutions Scaling considerations Next steps in development
