Andrew Bolis Curates Top Free Courses for Building AI Agents

In a recent LinkedIn post, Andrew Bolis addresses the growing interest in AI agents by providing a curated list of free courses for individuals looking to learn how to build them. Bolis highlights that while the discussion around AI agents is prevalent, practical learning resources are often overlooked.

As Andrew Bolis notes in his post:

“Everyone is discussing AI agents. But no one teaches you how to build them.”

Bolis’s post aims to fill this gap by offering a comprehensive, albeit selective, compilation of educational materials covering various facets of AI agent development. He emphasizes the importance of upskilling in this rapidly evolving field to unlock new career opportunities.

Key Resources for AI Agent Development

The core of Andrew Bolis’s contribution is his list of nine distinct free courses, each focusing on a specific aspect of AI agent creation. These resources are designed to guide learners from foundational concepts to more advanced applications.

Multi-Agent Systems and Collaboration

A significant portion of the recommended courses delves into the creation of multi-agent systems, where multiple AI agents collaborate to achieve complex tasks. Bolis includes:

• Multi AI Agent Systems with crewAI: This course focuses on building teams of agents that can automate multi-step tasks through collaboration.
• Introduction to LangGraph: This resource teaches the basics of using LangGraph to construct precise agentic and multi-agent workflows.
• AI Agents in LangGraph: A follow-up course that guides users on building and refining AI agents for enhanced control and search capabilities.
• AI Agentic Design Patterns with AutoGen: This course explores how to build and customize multi-agent AI systems for complex collaborative tasks.

According to Bolis, these courses are crucial for understanding how to orchestrate multiple AI entities to work together effectively.

Prompt Engineering and Foundational Concepts

Beyond system architecture, Bolis also highlights the importance of effective communication with AI models. He includes:

“Foundations of Prompt Engineering”

This course, as listed by Bolis, aims to teach the principles of crafting effective prompts to improve AI accuracy, automate workflows, and tailor outputs. He also points to the Large Language Model Agents MOOC, which covers how LLM agents function, including their reasoning capabilities, tool usage, and real-world applications.

Advanced Techniques and Memory

Bolis’s curated list extends to more specialized areas, such as Retrieval-Augmented Generation (RAG) and agent memory.

• Building RAG Agents with LLMs: This course covers the design of RAG systems, integrating LLMs with embeddings, tools, and dialog flow management.
• Building Agentic RAG with LlamaIndex: Focuses on creating intelligent research agents capable of multi-document reasoning and routing.
• LLMs as Operating Systems: Agent Memory: This course, according to Bolis, teaches how to implement persistent, self-managing memory for AI agents using Letta.

Andrew Bolis stresses the value of these advanced topics for those looking to build more sophisticated and capable AI agents.

Bolis concludes his post by encouraging readers to save the information for future reference and to follow him for more AI-related content. He also provides a link to a broader collection of free AI courses from major tech companies.