Agentic AI Development: Complete Learning Path
Overview
Master production-ready AI systems through hands-on projects covering RAG, multi-agent systems, and enterprise deployment.
Course Structure
Module 1: RAG-Powered AI App
Build a question-answering assistant with custom document knowledge.
Key Topics:
- LangChain fundamentals and architecture
- Vector database integration (Pinecone/Weaviate/Chroma)
- RAG pipeline design and optimization
- Conversation memory patterns
- Semantic retrieval techniques
Project: Question-answering assistant with semantic search and memory
Module 2: Stateful Agent Workflows
Design intelligent agents with LangGraph and state management.
Key Topics:
- LangGraph core concepts (state graphs, nodes, edges)
- Agent state persistence and checkpointing
- Loop patterns (self-correction, human-in-the-loop)
- Tool integration and error handling
- Complex workflow design with branching logic
Project: Dynamic decision-making agent with state management
Module 3: Multi-Agent System
Create collaborative systems with specialized agent roles.
Key Topics:
- Agent architecture patterns (ReAct, function calling)
- Agent specialization and role definition
- Inter-agent communication protocols
- Coordination strategies (supervisor, hierarchical teams)
- Multi-tool orchestration
Project: 3+ agent collaborative system with specialized roles
Module 4: External Tool Integration
Connect agents to external APIs and tools via MCP protocol.
Key Topics:
- MCP protocol fundamentals
- Custom tool development and schema design
- API integration patterns (REST, GraphQL)
- Error handling and retry strategies
- Performance optimization and caching
Project: MCP-connected agent with custom tool suite
Module 5: Evaluation & Testing
Implement comprehensive testing and evaluation frameworks.
Key Topics:
- RAGAS evaluation (faithfulness, relevancy, precision)
- DeepEval implementation and custom evaluators
- Unit and integration testing strategies
- Performance benchmarking
- A/B testing framework
Project: Comprehensive evaluation suite with metrics dashboard
Module 6: Security & Safety
Build secure systems with safety guardrails.
Key Topics:
- Input validation and prompt injection defense
- Output filtering and content moderation
- Rate limiting and access control
- Audit logging and compliance
- Safety guardrails and value alignment
Project: Hardened system with safety guardrails
Module 7: Production Deployment
Deploy applications with FastAPI and Streamlit.
Key Topics:
- FastAPI backend development
- Streamlit frontend design
- Docker containerization
- Cloud deployment (AWS/GCP/Azure)
- CI/CD pipeline setup
Project: Deployed application with FastAPI/Streamlit
Module 8: Observability & Monitoring
Implement comprehensive monitoring and logging.
Key Topics:
- LangSmith integration for tracing
- Structured logging architecture
- Metrics collection (latency, tokens, success rates)
- Error tracking and alerting (Sentry, PagerDuty)
- Performance dashboards with Grafana
Project: Monitoring and alerting system
Module 9: Resilience & Reliability
Build fault-tolerant systems with graceful degradation.
Key Topics:
- Error handling strategies
- Retry mechanisms with exponential backoff
- Fallback patterns
- Circuit breaker implementation
- Chaos engineering and stress testing
Project: Fault-tolerant system with graceful degradation
Module 10: Enterprise Compliance
Ensure compliance and professional documentation.
Key Topics:
- Data privacy compliance (GDPR)
- API documentation (OpenAPI/Swagger)
- System architecture documentation
- User guides and troubleshooting
- Compliance reporting and audit trails
Project: Compliant system with full documentation
Three Major Projects
Project 1: RAG-Powered Assistant
- Custom document knowledge base
- Semantic search with vector databases
- Conversation memory
- Real-time Q&A interface
Project 2: Multi-Agent Collaboration System
- 3+ specialized agents with distinct roles
- Inter-agent communication
- Coordinated problem-solving
- Multiple tool integrations
Project 3: Production-Ready Deployment
- Full testing suite (unit, integration, e2e)
- Monitoring and observability
- Security guardrails
- Professional documentation
- Cloud deployment with CI/CD
🛠️ Tech Stack
Frameworks: LangChain, LangGraph, FastAPI, Streamlit
Databases: Pinecone, Weaviate, ChromaDB
Evaluation: RAGAS, DeepEval
Monitoring: LangSmith, Grafana, Sentry
Deployment: Docker, AWS/GCP/Azure, GitHub Actions
Learning Outcomes
By completing this course, you will:
- ✅ Build production-ready RAG applications
- ✅ Design stateful agent workflows with LangGraph
- ✅ Create multi-agent systems with coordination
- ✅ Integrate external tools via MCP protocol
- ✅ Implement comprehensive testing and evaluation
- ✅ Deploy secure, monitored applications
- ✅ Handle errors and build resilient systems
- ✅ Ensure enterprise compliance and documentation
🎓 Prerequisites
- Python programming experience
- Basic understanding of APIs and web development
- Familiarity with machine learning concepts
- Git and command line proficiency
Time Commitment
Total Duration: 10-12 weeks
Per Module: 1 week
Hands-on Projects: 3-4 weeks
🔗 Resources
All code examples, datasets, and documentation are linked in the Resources section.
Start your journey to becoming an Agentic AI expert today! 🚀