30 Days with AI Agents: From Basics to Production

The definitive open-source curriculum for building production-grade AI agents

Why This Course?

The AI agent landscape is evolving rapidly, but quality learning resources remain fragmented. This course provides:

• Structured Learning Path: 30 days of carefully sequenced content, from fundamentals to production
• Production-First Mindset: Every concept taught with real-world deployment in mind
• Hands-On Projects: Daily coding exercises with production-quality reference implementations
• Open Source Tools: Focus on battle-tested, community-driven frameworks
• Best Practices: Security, testing, monitoring, and cost optimization built-in from day one

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Course Objectives

By completing this 30-day journey, you will be able to:

Skill Area	What You'll Master
Agent Fundamentals	Reasoning, planning, memory systems, and tool use patterns
Framework Proficiency	LangChain, LlamaIndex, CrewAI, and AutoGen
Model Optimization	Fine-tuning, PEFT/LoRA, quantization, and inference optimization
RAG Systems	End-to-end retrieval-augmented generation pipelines
Multi-Agent Systems	Orchestration, communication, and collaborative agent architectures
Multimodal Agents	Integrating vision models (LVMs) into agent workflows
Production Deployment	Containerization, Kubernetes, CI/CD, and monitoring
Safety & Security	Guardrails, prompt injection prevention, and alignment

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Course Structure

Phase 1: Foundations of AI Agents (Days 1-8)

Build a solid understanding of what agents are, how they work, and the core patterns that power them.

Day	Topic	Key Concepts
Day 1	Introduction to AI Agents	Agentic loop, core components, first agent
Day 2	Prompt Engineering for Agents	System prompts, few-shot learning, chain-of-thought, structured outputs
Day 3	Tool Use & Function Calling	Function schemas, tool selection, execution patterns
Day 4	Memory Systems	Short/long-term memory, conversation buffers, summary memory
Day 5	RAG Fundamentals	Document loading, chunking, embeddings, vector retrieval
Day 6	Multi-Agent Systems	Agent coordination, supervisor/worker patterns, shared memory
Day 7	Agent Testing & Evaluation	Unit testing, evaluation metrics, benchmarking
Day 8	Debugging & Observability	Tracing, logging, LangSmith, Phoenix

Phase 1 Outcome: You can build, test, and debug single-agent systems using industry-standard frameworks.

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Phase 2: Model Optimization & Serving (Days 9-19)

Master the techniques needed to make AI agents fast, efficient, and cost-effective in production.

Day	Topic	Key Concepts
Day 9	Introduction to LLM Fine-tuning	When to fine-tune, dataset preparation, training loops
Day 10	Parameter-Efficient Fine-Tuning (PEFT)	LoRA, QLoRA, adapter methods
Day 11	Quantization: Theory & Practice	INT8, INT4, GPTQ, AWQ
Day 12	Advanced Quantization & Model Pruning	Structured pruning, distillation, GGUF
Day 13	Introduction to Multimodal & LVMs	Vision-language models, architectures, use cases
Day 14	LVM Fine-tuning & Optimization	Training vision models, efficient inference
Day 15	Model Serving Fundamentals	REST APIs, FastAPI, request handling
Day 16	LLM Serving: TGI & vLLM	Continuous batching, PagedAttention, deployment
Day 17	Advanced Serving & Inference	Triton, ONNX Runtime, speculative decoding, caching
Day 18	Monitoring & Observability	Prometheus, Grafana, custom metrics, alerting
Day 19	Scalability & Cost Optimization	Auto-scaling, spot instances, token optimization

Phase 2 Outcome: You can optimize, quantize, and serve models efficiently with proper monitoring.

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Phase 3: Production-Grade Agent Systems (Days 20-30)

Build sophisticated, production-ready agent systems with advanced architectures.

Day	Topic	Key Concepts
Day 20	RAG Architecture & Chunking	Document processing, chunking strategies, embedding models
Day 21	Advanced RAG & Retrieval	Hybrid search, reranking, query transformation
Day 22	Agentic RAG & Tool-Augmented RAG	Self-RAG, CRAG, tool-augmented retrieval
Day 23	Multi-Agent Systems & Orchestration	Agent communication, CrewAI, AutoGen, workflows
Day 24	Multimodal Agents	Vision-language agents, document understanding
Day 25	Safety, Guardrails & Security	Prompt injection, output filtering, constitutional AI
Day 26	Building Full-Stack Agent Applications	Frontend integration, WebSocket, streaming
Day 27	Containerization with Docker	Dockerfiles, multi-stage builds, optimization
Day 28	Kubernetes & Orchestration	Deployments, services, scaling, GPU scheduling
Day 29	CI/CD for AI Agents	Testing pipelines, model versioning, rollback strategies
Day 30	Capstone: End-to-End Production Agent	Complete project integrating all learned concepts

Phase 3 Outcome: You can design, build, and deploy production-grade multi-agent systems.

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What Makes This Course Different?

1. Production-Quality Code

Every day includes reference implementations with:

• Comprehensive error handling
• Structured logging (JSON format)
• Type hints and documentation
• Unit tests with coverage reporting
• Configuration management

2. Real-World Focus

We don't just teach concepts—we show you how to:

• Handle failures gracefully
• Monitor agent behavior in production
• Optimize costs without sacrificing quality
• Secure your agents against attacks

3. Framework Agnostic Understanding

Learn the patterns behind the frameworks so you can:

• Choose the right tool for each job
• Migrate between frameworks when needed
• Build custom solutions when frameworks fall short

4. Community-Driven

• Open source and freely available
• Contributions welcome
• Regular updates to keep pace with the field
• Discussion forums for learners

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Prerequisites

Technical Requirements

Requirement	Details
Python	3.8+ with pip/conda
Git	For version control
Docker	For containerization (Phase 3)
GPU Access	Recommended for Days 9-14 (Colab Pro, HF Spaces, or local)

Knowledge Prerequisites

Level	Recommended Background
Required	Python programming, basic ML concepts
Helpful	REST APIs, Docker basics, cloud services
Optional	Kubernetes, CI/CD pipelines

Recommended Accounts

• Hugging Face: Model downloads and Spaces (Pro recommended for faster access)
• OpenAI or Anthropic: API access for some exercises
• GitHub: Version control and CI/CD

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Quick Start

# Clone the repository
git clone https://github.com/your-username/30-days-with-agents.git
cd 30-days-with-agents

# Start with Day 1
cd day_1

# Create and activate the environment
conda env create -f environment.yml
conda activate day1_hello_agent

# Verify your setup
python verify_setup.py

# Run your first agent!
python hello_agent.py

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Repository Structure

30-days-with-agents/
├── README.md                    # This file
├── CONTRIBUTING.md              # Contribution guidelines
├── LICENSE                      # MIT License
│
├── day_1/                       # Day 1: Introduction to AI Agents
│   ├── README.md                # Daily guide
│   ├── hello_agent.py           # Main implementation
│   ├── example_usage.py         # Usage examples
│   ├── verify_setup.py          # Setup verification
│   ├── run_tests.py             # Test runner
│   ├── config.yaml              # Configuration
│   ├── environment.yml          # Conda environment
│   ├── requirements.txt         # Pip dependencies
│   ├── docs/                    # Additional documentation
│   └── tests/                   # Unit tests
│
├── day_2/                       # Day 2: Prompt Engineering
│   └── ...
│
└── day_N/                       # Each day follows similar structure
    └── ...

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Learning Path Recommendations

For Beginners

Follow the course sequentially. Each day builds on the previous one.

For Experienced Developers

• Know Python + ML basics? Start at Day 1, but move quickly through Phase 1
• Already use LangChain/LlamaIndex? Skim Days 5-6, focus on Days 7-8
• Want production skills? Focus on Phase 2 (Days 9-19) and Phase 3 (Days 20-30)

For Teams

• Week 1-2: Have all team members complete Phase 1 together
• Week 3-4: Split by specialty (some focus on optimization, others on deployment)
• Week 5: Reconvene for Phase 3 capstone project

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Daily Time Commitment

Activity	Time
Reading & concepts	30-45 minutes
Hands-on coding	1-2 hours
Exercises & experiments	30-60 minutes
Total per day	2-4 hours

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Support & Community

Getting Help

• Issues: GitHub Issues for bugs and questions
• Discussions: GitHub Discussions for general questions
• Stack Overflow: Tag questions with ai-agents and 30-days-course

External Communities

• LangChain Discord
• Hugging Face Forums
• r/MachineLearning
• r/LocalLLaMA

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Ways to Contribute

• Fix typos and improve documentation
• Add more examples and exercises
• Report bugs and suggest improvements
• Translate content to other languages
• Share your projects built with this course

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Citation

If you use this course in your research or teaching, please cite:

@misc{30daysaiagents2024,
  title={30 Days with AI Agents: From Basics to Production},
  author={Contributors},
  year={2024},
  howpublished={\url{https://github.com/your-username/30-days-with-agents}}
}

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Acknowledgments

This course draws inspiration from:

• The open-source AI community
• LangChain, LlamaIndex, and Hugging Face teams
• Countless blog posts, papers, and tutorials from practitioners worldwide

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Ready to build the future of AI agents?

Start Day 1 →

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Built with care by the community, for the community.