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Course Outline
Advanced LangGraph Architecture
- Graph topology patterns: nodes, edges, routers, and subgraphs
- State modeling: channels, message passing, and persistence
- Understanding DAG versus cyclic flows and hierarchical composition
Performance and Optimization
- Parallelism and concurrency patterns in Python
- Caching, batching, tool calling, and streaming techniques
- Strategies for cost control and token budgeting
Reliability Engineering
- Implementing retries, timeouts, backoff, and circuit breaking
- Ensuring idempotency and deduplication of steps
- Checkpointing and recovery using local or cloud storage solutions
Debugging Complex Graphs
- Step-through execution and dry run capabilities
- Inspecting state and tracing events
- Reproducing production issues using seeds and fixtures
Observability and Monitoring
- Structured logging and distributed tracing
- Key operational metrics: latency, reliability, and token usage
- Setting up dashboards, alerts, and tracking SLOs
Deployment and Operations
- Packaging graphs as services and containers
- Managing configurations and handling secrets
- Implementing CI/CD pipelines, rollouts, and canary deployments
Quality, Testing, and Safety
- Conducting unit testing, scenario testing, and automated evaluation harnesses
- Applying guardrails, content filtering, and handling PII
- Performing red teaming and chaos experiments to ensure robustness
Summary and Next Steps
Requirements
- Understanding of Python and asynchronous programming
- Experience in developing LLM applications
- Familiarity with fundamental LangGraph or LangChain concepts
Audience
- AI platform engineers
- DevOps specialists for AI
- ML architects managing production LangGraph systems
35 Hours
