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Duration 14 hours
Course Outline
LangGraph and Agent Patterns: A Practical Introduction
- Graphs versus linear chains: timing and rationale
- Agents, tools, and planner-executor cycles
- Basic workflow: a minimal agentic graph
State Management, Memory, and Context Handling
- Defining graph state and node interfaces
- Differentiating between short-term and persistent memory
- Managing context windows, summarization, and data rehydration
Branching Logic and Control Flow
- Conditional routing and multi-path decision-making
- Implementing retries, timeouts, and circuit breakers
- Handling fallbacks, dead-ends, and recovery mechanisms
Tool Utilization and External Integrations
- Invoking functions and tools from nodes and agents
- Accessing REST APIs and databases within the graph
- Parsing and validating structured outputs
Retrieval-Augmented Agent Workflows
- Document ingestion and chunking methodologies
- Using embeddings and vector stores like ChromaDB
- Generating grounded responses with citations and safety guards
Evaluation, Debugging, and Observability
- Tracing execution paths and analyzing node interactions
- Creating golden sets, evaluations, and regression tests
- Monitoring quality, safety, and cost/latency metrics
Packaging and Deployment
- Serving via FastAPI and managing dependencies
- Versioning graphs and implementing rollback plans
- Developing operational playbooks and incident response protocols
Summary and Future Steps
Requirements
- Practical proficiency in Python
- Hands-on experience developing LLM applications or prompt chains
- Understanding of REST APIs and JSON structures
Target Audience
- AI engineers
- Product managers
- Developers engineering interactive, LLM-driven systems
