Intro to AI Agents: Build an Army of Digital Workers with AI Course Syllabus

Overview: This course provides a practical introduction to AI agents, guiding you from foundational concepts to building and deploying autonomous digital workers. You'll explore key frameworks like LangChain and AutoGen, learn how to integrate agents with external tools and APIs, and apply your skills to real-world automation challenges. With approximately 6 hours of content, the course is structured into hands-on modules that build progressively, culminating in practical applications across business use cases and deployment scenarios.

Module 1: Introduction to AI Agents

Estimated time: 0.5 hours

• What are AI agents and why they matter in automation
• Understanding autonomous workflows and agent capabilities
• Overview of key frameworks: LangChain, AutoGen, and others
• Use cases and future potential of AI agents

Module 2: LLM Fundamentals for Agent Building

Estimated time: 0.75 hours

• Role of large language models (LLMs) in agent workflows
• Understanding tokens, context windows, and model limitations
• Basics of prompt engineering for agent reliability
• Managing input/output in dynamic agent environments

Module 3: Building a Basic AI Agent

Estimated time: 1 hour

• Creating a single-agent system using LangChain
• Connecting agents to external APIs
• Handling dynamic input and output
• Testing and debugging simple agent behavior

Module 4: Multi-Agent Collaboration

Estimated time: 1 hour

• Designing agents that communicate and collaborate
• Implementing agent roles and responsibilities
• Planning and delegation using agent hierarchies
• Orchestrating workflows between multiple agents

Module 5: Tool Integration & File Management

Estimated time: 1 hour

• Equipping agents with tools: Python execution, file readers
• Integrating search APIs and web data retrieval
• Automating file-based tasks: reading, writing, processing
• Connecting agents to databases and structured data sources

Module 6: Real-World Use Cases of AI Agents

Estimated time: 1.25 hours

• Case study: Business process automation
• Document summarization and analysis workflows
• Evaluating agent performance and accuracy
• Handling errors and edge cases in production

Module 7: Agent Deployment & Hosting

Estimated time: 0.75 hours

• Deploying agents using FastAPI or Streamlit
• Setting up hosted environments for agent access
• Scalability and monitoring considerations

Module 8: Responsible AI & Limitations

Estimated time: 0.5 hours

• Ethical considerations in autonomous agent design
• Preventing prompt injection and misuse
• Understanding current limitations and risks

Prerequisites

• Familiarity with Python programming
• Basic understanding of APIs and web services
• Access to a development environment with Python and package managers

What You'll Be Able to Do After

• Explain the core concepts and architecture of AI agents
• Build functional AI agents using LangChain and AutoGen
• Integrate agents with external tools, APIs, and data sources
• Design multi-agent systems for collaborative task execution
• Deploy AI agents for real-world automation and business applications