Welcome to the Agentic AI Mentorship Program! This repository is designed to help students and developers systematically evolve their AI agent development skills from foundational prompting techniques to advanced multi-agent systems through hands-on, build-style exercises and real-world scenarios.
This program provides a structured, progressive learning path for Agentic AI development. It covers advanced prompting techniques, agentic workflow patterns, agent building with tools and memory, and sophisticated multi-agent system orchestration. Each module contains step-by-step rubrics, acceptance criteria, and recommended best practices, making it ideal for self-study, bootcamps, or guided mentorship.
The repository is organized into four main levels corresponding to the Agentic AI Nanodegree curriculum:
Foundational prompting techniques that power modern AI agents. Master Chain-of-Thought, ReAct, and feedback loops to build systems that can reason, plan, and solve complex problems.
Key Topics:
- Introduction to Agentic AI concepts and environment setup
- Role-based prompting for persona control
- Chain-of-Thought (CoT) and ReAct prompting frameworks
- Prompt instruction refinement techniques
- Prompt chaining for multi-step workflows
- LLM feedback loops for self-improving systems
- Project: Trip Planner - A Multi-Agent Travel Assistant System
Architect intelligent systems using core agentic workflow patterns. Learn to design and build teams of AI agents using Prompt Chaining, Routing, Parallelization, and advanced orchestration patterns.
Key Topics:
- Agentic workflow fundamentals and modeling
- Prompt Chaining workflows for sequential task decomposition
- Routing patterns for task classification and delegation
- Parallelization for concurrent agent execution
- Evaluator-Optimizer workflows for iterative refinement
- Orchestrator-Workers patterns for dynamic planning
- Project: AI-Powered Agentic Workflow for Project Management
Build robust AI agents with tool integration, structured outputs, state management, and memory systems. Create data-driven agents that interact with external APIs, databases, and perform advanced retrieval tasks.
Key Topics:
- Tool integration via function calling
- Structured outputs with Pydantic validation
- Agent state management and transitions
- Short-term and long-term memory systems
- External API and database integration
- Web search and agentic RAG implementation
- Agent evaluation and performance metrics
- Project: GamerPlay - An AI Research Agent for the Video Game Industry
Master coordinated teams of AI agents. Learn to orchestrate complex workflows, manage distributed state, implement advanced routing, and build production-ready multi-agent systems.
Key Topics:
- Multi-agent architecture design and implementation
- Agent orchestration and activity coordination
- Advanced routing and data flow management
- Distributed state management and coordination
- Multi-agent RAG for specialized retrieval
- Production deployment and monitoring
- Project: The Woody Paper Company Sales Team
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Prerequisites: Ensure you have OpenAI API access, basic Python knowledge, and familiarity with generative AI concepts.
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Start at your level: Begin with the appropriate level based on your experience:
- New to AI agents? Start with 01_prompting_for_effective_llm_reasoning_and_planning
- Know basic prompting? Jump to 02_agentic_workflows
- Understand workflows? Begin with 03_building_agents
- Want advanced systems? Start with 04_multi_agent_systems
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Follow the progression: Each lesson within a level builds on the previous. Complete exercises in order for the smoothest learning experience.
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Practice hands-on: Each exercise requires implementing working code. Don't just read - build and test!
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Meet acceptance criteria: Each exercise has specific, verifiable outcomes. Ensure you meet all criteria before proceeding.
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Complete projects: End-of-level projects synthesize all lesson concepts into comprehensive, portfolio-worthy applications.
# Create virtual environment
python -m venv agentic_ai_env
source agentic_ai_env/bin/activate # On Windows: agentic_ai_env\Scripts\activate
# Install core dependencies
pip install openai python-dotenv pydantic requests
# Optional but recommended
pip install jupyter pytest chromadb sqlalchemy tavily-pythonCreate a .env file in the root directory:
OPENAI_API_KEY=your_openai_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here # For web search exercises
agentic_ai_mentorship_program/
├── 01_prompting_for_effective_llm_reasoning_and_planning/
│ ├── L01_introduction_to_prompting/
│ ├── L02_role_of_prompting_in_agentic_ai/
│ ├── ...
│ └── project_agentsville_trip_planner/
├── 02_agentic_workflows/
├── 03_building_agents/
├── 04_multi_agent_systems/
├── shared_utils/ # Common utilities across all levels
├── examples/ # Reference implementations
└── tests/ # Automated test suites
Each exercise includes:
- Objective: Clear learning goal
- Build Steps: Detailed implementation guidance
- Acceptance Criteria: Specific, testable outcomes
- Extension Challenges: Optional advanced features
- ✅ Basic: Meets all acceptance criteria
- 🚀 Advanced: Completes extension challenges
- 🏆 Mastery: Creates innovative variations
- AI Engineers seeking structured agent development skills
- Software Developers transitioning to AI agent systems
- Product Managers wanting hands-on AI agent experience
- Researchers exploring practical agent implementations
- Students in AI/ML programs focusing on agent technologies
By completing this program, you will:
- Master Prompting Techniques: Chain-of-Thought, ReAct, role-based prompting, and feedback loops
- Design Agentic Workflows: Implement routing, parallelization, and orchestration patterns
- Build Production Agents: Tool integration, state management, memory systems, and evaluation
- Orchestrate Multi-Agent Systems: Coordinate teams of specialized agents for complex tasks
- Deploy Real Applications: Create portfolio-worthy projects demonstrating agent capabilities
- 📚 Documentation: Comprehensive guides and API references
- 💬 Discussions: Share solutions and get help from peers
- 🐛 Issues: Report bugs or suggest improvements
- 🔗 Resources: Curated links to papers, tools, and frameworks
This project is licensed under the GNU General Public License v3.0 (GPL-3.0) for personal and non-commercial use only.
For personal, educational, and non-commercial purposes, this project is freely available under the GPL-3.0 license:
✅ You Can:
- Use these materials for personal projects and learning
- Modify and adapt the materials for non-commercial purposes
- Contribute improvements back to the project
- Disclose source and include license notices
- Share modifications under the same GPL-3.0 license
- Clearly state any significant changes made to original materials
❌ You Cannot:
- Sublicense under different terms
- Hold authors liable for damages
Commercial use of this software requires a separate commercial license.
Commercial use includes, but is not limited to:
- Integration into commercial products or services
- Use within organizations generating revenue
- Deployment in enterprise or production environments for business purposes
- Distribution as part of commercial offerings
For commercial licensing inquiries, please contact inbox.
We offer flexible commercial licensing options tailored to your organization's needs, including support and maintenance agreements.
The GPL-3.0 license terms for non-commercial use can be found in the LICENSE file.
Copyright (C) 2022-2026 flickleafy
This program is free software for personal use: you can redistribute it
and/or modify it under the terms of the GNU General Public License as
published by the Free Software Foundation, either version 3 of the License,
or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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Commercial use requires a separate commercial license. Please contact
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Start your journey into the future of AI agent development. Build intelligent systems that can reason, plan, and act autonomously to solve real-world problems.