Foundations of Multi-Agent Automation with CrewAI
A Pragmatic, Hands-On Introduction to Building Collaborative AI Agents
Duration
2 Day
Level
Basic Level
Design and Tailor this course
As per your team needs
Overview
This immersive workshop introduces participants to the principles and practicalities of architecting, developing, and deploying multi-agent AI systems using the open-source CrewAI framework. Through a combination of interactive lectures, live demonstrations, and guided labs, attendees will learn to configure collaborative “crews” of agents, define specialized roles, orchestrate complex workflows (serial, parallel, conditional), and integrate essential tools such as web search, file I/O, and advanced memory stores.
Audience
This course is designed for professionals and students seeking practical, production-oriented skills in multi-agent automation:
- Developers (AI/ML engineers, automation developers)
- Data Engineers
- Automation Architects
- Solution Consultants
- Technical Managers adopting AI-driven workflows
- Beginner and intermediate technologists eager to upskill in agentic AI
Prerequisites
- Programming: Basic proficiency in Python (version 3.8+ recommended)
- Command-Line: Familiarity with CLI usage on Linux, macOS, or Windows terminals
- Version Control: Experience with Git/GitHub for code management and collaboration
- APIs: Understanding of RESTful API concepts is beneficial but not mandatory
Curriculum
- AI Agent Landscape
- Evolution: From single-LLM calls to orchestrated multi-agent workflows
- Value proposition for businesses and technical teams
- Competitive Overview
- How CrewAI compares to AutoGPT, LangChain Agents, and other frameworks
- CrewAI Cloud Essentials
- No-code Studio overview vs. code-centric approaches
- Templates for rapid prototyping and business impact (ROI, time savings)
- Use-Case Workshop
- Identify 2–3 automation candidates in your organization
- Estimate potential impact (FTE savings, cycle-time reduction)
- Group discussion and short presentations
- Studio Walkthrough
- Creating Crew projects from ready-made templates (e.g., Research, Support, Reporting)
- Drag-and-drop orchestration, parameter binding
- CLI & SDK Introduction
- Initializing projects via CLI (`crewai init`)
- Importing/exporting between Studio and code
- Local dry runs and validation (`crewai run –mode dry-run`)
- Hands-On Lab
- Build a “Hello, World” Crew in Studio
- Step-by-step: Extend to perform text summarization with OpenAI or Azure LLM
- Role & Task Definition
- Designing specialist roles: Researcher, Drafter, Editor, Validator
- Backstories, objectives, and success criteria for agents
- Orchestration Patterns
- Serial pipelines with checkpoints
- Parallel execution and fan-in/fan-out scalability
- Conditional branching based on content or confidence levels
- Inter-Agent Communication
- Shared memory vs. explicit message passing
- Strategies for conflict resolution and duplicate work prevention
- Hands-On Lab
- Design and deploy a research-article workflow (Research → Draft → Fact-Check)
- Built-In Tools
- Web search integration (Bing/Google)
- PDF and text search, file I/O (CSV/JSON)
- Notifications via Email/Slack
- Memory & State
- CrewAI memory stores: In-Cloud, Redis, etc.
- Context management and semantic retrieval
- Guardrails & Error Handling
- Configuring system prompts (tone, style, brand voice)
- Implementing retry logic, fallbacks, and human-in-the-loop safety checks
- Hands-On Lab
- Add memory persistence to the Article crew
- Implement guardrails to enforce quality/output standards
- Intake Agent: Parse tickets from JSON/CSV and classify urgency
- Context Agent: Lookup prior tickets & FAQ responses
- Response Agent: Generate appropriate, brand-aligned replies
- Deployment Options
- Serverless execution, self-hosted alternatives (Kubernetes, Azure, GCP)
- Auto-generated UI endpoints for end-user interaction
- Monitoring and Metrics
- Real-time dashboarding: latency, success rates, cost per call
- Alerting for SLA breaches and anomalies
- A/B Testing & Version Control
- Cloning crews for experiments
- Safe rollbacks and canary deployments
- Hands-On Lab
- Deploy Article crew to test/production environment
- Set up dashboards and alerts; run an A/B prompt test
- Access Control & Security
- Role-Based Access Control (RBAC), granular tool permissions
- Secrets management (Vault, Azure Key Vault)
- Audit Trails & Compliance
- Immutable logs, traceability, state-change recording
- Data retention and export policies
- Integration Patterns
- Embedding crews in web/mobile apps
- Webhooks, REST APIs, event triggers for automation
Duration
2 Day
Level
Basic Level
Design and Tailor this course
As per your team needs
