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Instructor-led training / ai-agentic-crewa

Multi-Agent Systems with CrewAI

Master production-grade agentic AI with CrewAI by building modular role-based teams, custom grounded tools, event-driven flows, and enterprise guardrails in a hands-on, 2-day workshop.
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Instructor-led

ai-agentic-crewa

Duration
2 Days
Format
Live Online
Access
Multiple Timezones

Overview

About this course.

This 2-day hands-on workshop teaches engineers how to design, orchestrate, and deploy collaborative multi-agent teams using CrewAI. Participants learn how to move beyond single-prompt setups by structuring specialized roles, managing sequential and event-driven workflows with CrewAI Flows, integrating custom tools, and implementing enterprise guardrails for production-grade reliability.

Objectives

What you'll learn

  • Transition from monolithic prompting to modular, role-based agent architectures using CrewAI primitives.
  • Build, ground, and safely deploy custom tools using BaseTool and type-hinted decorators.
  • Implement deterministic, event-driven workflows using CrewAI Flows and manage persistent state.
  • Apply enterprise guardrails, human-in-the-loop checkpoints, and open-source observability tracing.
  • Orchestrate an end-to-end autonomous research flow in a hands-on capstone project.

Audience

Who it's for

  • Software Engineers
  • Tech Leads
  • AI Application Developers

Prerequisites

Before you start

  • Proficiency in Python
  • Basic familiarity with REST APIs
  • Understanding of basic LLM prompting

Outline

Course outline.

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1. Foundations of Role-Based Multi-Agent Systems

  • From Single Prompts to Agent Teams: Transitioning from monolithic prompting to modular, role-based agent architectures.
  • Core Mental Model: Deconstructing the CrewAI primitives: Agents, Tasks, Tools, and Crews.
  • Defining Personas & Model Selection: Configuring role, goal, and backstory; matching task complexity to frontier vs. lighter models.
  • Task Contracts & Structured Outputs: Enforcing explicit expected outcomes and output schemas using Pydantic for reliable downstream parsing.

2. Custom Tools, Environment & Safety

  • Rapid Environment Setup: Utilizing pre-configured .env boilerplate and project scaffolding to manage credentials securely without setup friction.
  • Building Custom Tools: Subclassing BaseTool and applying type-hinted decorators to expose local databases, web scrapers, and internal APIs.
  • Tool Grounding & Exception Handling: Preventing hallucinated parameters, handling runtime API errors gracefully, and setting tool limits to eliminate infinite retry loops.

3. Advanced Orchestration, Flows & Memory

  • Orchestration Paradigms: Knowing when to use Hierarchical Managers (LLM-driven delegation) versus CrewAI Flows (deterministic, event-driven control).
  • Event-Driven Workflows with CrewAI Flows: Structuring production logic using @start(), @listen(), and @router() decorators for dynamic branching.
  • State & Memory Management: Managing persistent flow state, passing structured Pydantic payloads across execution boundaries, and configuring entity memory.

4. Enterprise Guardrails, Observability & Capstone

  • Production Guardrails: Setting iteration limits, rate-limiting LLM calls, and implementing Human-in-the-Loop (HITL) approval steps for sensitive task execution.
  • Observability & Tracing: Instrumenting open-source tracing (Langfuse / Phoenix) for token usage, latency analysis, and execution debugging.
  • 3-Hour Capstone Project: Complete an Autonomous Research & Intelligence Flow. Working from a skeletal framework, engineers write custom tool logic, configure task validation contracts, implement human-in-the-loop validation, and emit a structured, multi-source research report.

For teams

Training a whole team?

Request private training for Multi-Agent Systems with CrewAI and we'll follow up with a quote for your group.

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