Course Overview
About Course
This 40‑hour regime, crafted by Rabbitt Learning and others, equips beginners and intermediate developers to build autonomous multi-agent AI systems using CrewAI and Microsoft AutoGen Learners explore core Agentic AI concepts—reactive, goal-based, utility, and learning agents—understanding what differentiates them from basic automation
The curriculum spans agent architecture (planning loops, reflection, memory, tool use, and guardrails), role-based orchestration with CrewAI, and conversational multi-agent flows via AutoGen—including tool-calling and nested dialogues real-world projects guide hands-on development: a financial advisor, hiring assistant, code optimization agent, and content pipeline.
Ethical AI is integral, covering bias, fairness, transparency, and auditability. Capstone work and career coaching—resume style-ups, mock interviews—ensure readiness for roles like AI Workflow Engineer With tools, patterns, and full-stack deployment—from CI/CD to observability—graduates learn to design, develop, and productionize agentic systems responsibly and effectively
By course end, participants master designing autonomous workflows, integrating memory and tools, layering guardrails, and orchestrating production-grade agent ecosystems with ethical foundations and professional career assets.
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Course Syllabus
- Introduction to Agentic AI (4h)
- Description: Define agentic vs. traditional automation; explore agent types—reactive, goal-based, utility-driven, and learning agents.
- Outcome: Understand when and why autonomous agents matter
- Agent Architectures & Design Patterns (6h)
- Description: Delve into planning loops, memory (short/long-term), reflection, tool use, ReAct, guardrails.
- Outcome: Build robust agent brains using key patterns like reflection and planning
- CrewAI Essentials: Role-Based Orchestration (6h)
- Description: Learn to construct agent crews (e.g., researchers, writers, QA) working in parallel, hierarchical workflows.
- Outcome: Orchestrate role-specialized agents with memory and tool integrations
- AutoGen Studio Fundamentals (6h)
- Description: Use Microsoft AutoGen to choreograph conversational agent interactions, tool-calling, and chain orchestration.
- Outcome: Deploy multi-agent dialogs, nested exchanges, and dynamic workflows
- Project I – Financial Decision Agent (4h)
- Description: Build a CrewAI + AutoGen system that ingests financial data, uses tool calls, and outputs investment recommendations.
- Outcome: Data-oriented agent execution with tool use & memory.
- Project II – AI-Powered Hiring Assistant (4h)
- Description: Create a pipeline: shortlist, generate interview scripts, and conduct candidate evaluation.
- Outcome: End-to-end hiring workflow via orchestrated agent roles.
- Project III – Code Optimization Agent (4h)
- Description: Develop an agent to parse, refactor, and QA-check code autonomously.
- Outcome: Code review/build automation using agents.
- Project IV – Content Generation Pipeline (4h)
- Description: Implement agents for ideation, drafting, editing, and style adaptation in content workflows.
- Outcome: Multi-agent content creation scaffold.
- Ethics, Governance & Risk Management (4h)
- Description: Address bias, fairness, legal accountability, audit logging, and multi-agent safety guardrails.
- Outcome: Responsible, transparent systems design 10. Deployment & Monitoring (4h)
- Description: Production readiness: CI/CD, logging, alerting, dashboarding, resilience, and observability.
- Outcome: Operational AI agents in production.
Capstone Project (2h lecture + 6h project)
- Description: Learner scopes and builds a fully autonomous agent system, with peer reviews and instructor feedback.
- Outcome: Portfolio-ready agentic AI application.
Bonus: Career Prep & Coaching (4h)
- Description: Resume enhancements, mock interviews, and role-play as an “AI Workflow Engineer.”
- Outcome: Career-launch support for agentic AI roles.
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Key Features
Project‑First Approach: Four categories of real-world projects plus a comprehensive capstone
CrewAI + AutoGen Combo: Best-practice orchestration using both frameworks
Design Patterns Focus: Leveraging reflection, planning, tool use, and multi-agent coordination
Domain Applications: Finance, hiring, devops, content pipelines
Ethics & Governance: Fairness, transparency, auditability
Production Emphasis: Logging, CI/CD, resilience, monitoring
Support Ecosystem: Live labs, code examples, community access
Career Support: Portfolio coaching and mock interview sessions



