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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
- Strategies for caching, batching, tool calling, and streaming
- Cost controls and token budgeting techniques
Reliability Engineering
- Implementing retries, timeouts, backoff strategies, and circuit breaking
- Ensuring idempotency and deduplicating steps
- Utilizing checkpointing and recovery with local or cloud storage
Debugging Complex Graphs
- Conducting step-through execution and dry runs
- Inspecting state and tracing events
- Reproducing production issues using seeds and fixtures
Observability and Monitoring
- Implementing structured logging and distributed tracing
- Tracking operational metrics: latency, reliability, and token usage
- Setting up dashboards, alerts, and SLO tracking
Deployment and Operations
- Packaging graphs as services and containers
- Managing configuration and handling secrets
- Implementing CI/CD pipelines, rollouts, and canary deployments
Quality, Testing, and Safety
- Developing unit tests, scenario tests, and automated evaluation harnesses
- Applying guardrails, content filtering, and PII handling
- Conducting red teaming and chaos experiments for robustness
Summary and Next Steps
Requirements
- Proficiency in Python and asynchronous programming
- Practical experience in LLM application development
- Familiarity with fundamental LangGraph or LangChain concepts
Audience
- AI platform engineers
- DevOps professionals specializing in AI
- ML architects managing production LangGraph systems
35 Hours
