CrewAI Masterclass: Complete Multi-Agent Guide with Praveen Kumar Moses

Master the end-to-end engineering of production multi-agent systems with this comprehensive 6-Phase CrewAI Masterclass. Taught through practical, architecture-driven engineering blueprints in an accessible Tanglish (Tamil-English) technical style, this bundle takes you from core agent personas to enterprise-scale distributed workflows.

What You Get Inside (6 Core Modules / 33 Lessons)

• Phase 1: CrewAI Primitives & Foundations
  • Persona engineering (role, goal, backstory) vs generic prompting
  • Task definitions, Pydantic guardrails, and single Agent.kickoff() execution
  • Multi-LLM routing via native provider SDKs and llm_overlay
• Phase 2: Tools, MCP, Skills & Guardrails
  • Sandboxed code execution with Daytona & E2B
  • Custom tools (BaseTool & @tool) with typed schemas and opt-in caching
  • Native Model Context Protocol (MCP) over stdio & SSE
  • Real-world: 3-agent Investment Research Crew
• Phase 3: Process Orchestration & Workflows
  • Sequential task chaining and structured context passing
  • Hierarchical execution with dynamic manager LLMs & business SOPs
  • Non-blocking parallel execution (async_execution=True) and structured JSON outputs
• Phase 4: Unified Memory, Knowledge & Context Management
  • LanceDB-backed unified memory (remember(), recall(), forget())
  • Hierarchical scopes, composite scoring weights, and recency half-life tuning
  • Pre-seeding agents via RAG knowledge sources
  • Human-in-the-Loop (HITL) feedback patterns
• Phase 5: Event-Driven Flows, CLI & Evaluation
  • JSON-first project architecture (crew.jsonc & agents/*.jsonc)
  • Event-driven DAG flows (@start, @listen, @router, and_, or_)
  • SQLite state persistence, checkpointing, replay, and deep evaluation (crewai eval)
  • Distributed OpenTelemetry tracing
• Phase 6: Enterprise Deployment & System Design
  • High-throughput serving via FastAPI ASGI & Celery background workers
  • Real-time Server-Sent Events (SSE) streaming for frontends
  • Agent-to-Agent (A2A) distributed interoperability and cloud governance
  • Capstone: 10-Agent Autonomous Customer Acquisition Engine System Design

Who Is This For?

• Backend & Python Engineers moving into production GenAI and multi-agent systems
• AI/ML Engineers transitioning beyond basic prompt chaining to robust agent teams
• Technical Leaders & Architects preparing for Senior, Staff, or Lead Multi-Agent System Design interviews