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Duration 35 hours
Course Outline
Advanced LangGraph Architecture
- Graph topology patterns: nodes, edges, routers, and subgraphs
- State modeling techniques: channels, message passing, and persistence
- Distinguishing DAG from cyclic flows and implementing hierarchical composition
Performance and Optimization
- Implementing parallelism and concurrency patterns in Python
- Leveraging caching, batching, tool calling, and streaming
- Strategies for cost controls and token budgeting
Reliability Engineering
- Managing retries, timeouts, backoff, and circuit breaking
- Ensuring idempotency and deduplication of processing steps
- Checkpointing and recovery using local or cloud-based stores
Debugging Complex Graphs
- Utilizing step-through execution and dry runs
- Performing state inspection and event tracing
- Reproducing production issues using seeds and fixtures
Observability and Monitoring
- Implementing structured logging and distributed tracing
- Tracking operational metrics: latency, reliability, and token usage
- Configuring dashboards, alerts, and SLO tracking
Deployment and Operations
- Packaging graphs as services and containers
- Managing configurations and handling secrets
- Setting up CI/CD pipelines, rollouts, and canary releases
Quality, Testing, and Safety
- Building unit, scenario, and automated evaluation harnesses
- Applying guardrails, content filtering, and PII handling
- Conducting red teaming and chaos experiments for robustness
Summary and Next Steps
Requirements
- Solid understanding of Python and asynchronous programming patterns
- Practical experience in developing LLM applications
- Familiarity with fundamental LangGraph or LangChain concepts
Target Audience
- AI platform engineers
- DevOps professionals specializing in AI
- ML architects responsible for production LangGraph systems
