Multi AI Agent Systems with crewAI provides a practical introduction to designing and implementing multi-agent AI systems, where several specialized AI agents collaborate to complete complex, multi-step tasks. Developed by DeepLearning.AI in collaboration with crewAI and taught by crewAI founder João Moura, the course demonstrates how agent-based architectures can extend the capabilities of a single Large Language Model by distributing responsibilities across agents with different roles, goals, tools, and expertise.

The course introduces the fundamental building blocks of an AI agent and how these can be implemented using the crewAI framework. Each agent can be assigned a specific role, goal, and backstory that influence how it approaches its tasks. Complex processes can then be decomposed into well-defined tasks and assigned to specialized agents, creating a structured workflow rather than relying on a single LLM prompt to perform the entire process.

A central part of the course is understanding how agents become more capable through tools and memory. Agents can use pre-built or custom tools to access external capabilities such as web search and other information sources. Memory allows agents to maintain short-term, long-term, and shared information, providing additional context for decisions and collaboration. Guardrails are introduced as a mechanism for controlling agent behaviour and reducing problems such as errors, hallucinations, and infinite execution loops.

The course then extends individual agents into collaborative multi-agent systems. Agents can perform tasks sequentially, in parallel, or through hierarchical structures, allowing different agents to contribute their specialized capabilities to a shared objective. Delegation and cooperation enable one agent’s output to become input or context for another, creating workflows that resemble teams of people performing different roles within a business process.

Overall, the course provides a practical foundation for understanding multi-agent systems as an evolution of LLM-based applications. It demonstrates how role specialization, task decomposition, tools, memory, guardrails, and collaboration can be combined to automate workflows that require multiple stages of research, analysis, decision-making, and content generation.

Topics and techniques

• AI Agents – defining specialized roles, goals, and behaviours.
• crewAI – building and orchestrating teams of LLM-based agents.
• Tasks and delegation – decomposing complex processes into agent responsibilities.
• Tools – extending agents with external information and capabilities.
• Memory – short-term, long-term, and shared context between agents.
• Guardrails – controlling errors, hallucinations, and unwanted execution behaviour.
• Multi-Agent Collaboration – coordinating specialized agents within shared workflows.
• Sequential, parallel, and hierarchical processes – alternative patterns for organizing agent execution.

Practical exercises

The practical exercises use Python and crewAI to build multi-agent systems around realistic business processes. The first application creates a team of agents that collaborates to research, write, and edit a technical article, establishing the basic concepts of agents, tasks, roles, and collaboration. 

The exercises then progress through a customer support system, a customer outreach campaign using external tools, and an event-planning workflow. More advanced examples use cooperating agents for financial analysis and build a crew that analyzes job requirements, adapts a résumé, and prepares a candidate for an interview. Together, the exercises demonstrate six different business processes and show how multi-agent architectures can distribute complex work among specialized AI agents.