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Duration 35 hours
Course Outline
Advanced LangGraph Architecture
- Graph topology patterns: nodes, edges, routers, and subgraphs
- State modeling: channels, message passing, and persistence strategies
- Comparing DAG versus cyclic flows and hierarchical composition
Performance and Optimization
- Leveraging parallelism and concurrency patterns in Python
- Implementing caching, batching, tool calling, and streaming
- Managing costs and token budgeting strategies
Reliability Engineering
- Configuring retries, timeouts, backoff, and circuit breaking
- Ensuring idempotency and deduplication of workflow steps
- Checkpointing and recovery mechanisms using local or cloud-based stores
Debugging Complex Graphs
- Executing step-through runs and performing 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
- Utilizing dashboards, alerts, and SLO tracking
Deployment and Operations
- Packaging graphs as services and containers
- Managing configuration and handling secrets securely
- Establishing CI/CD pipelines, rollouts, and canary deployments
Quality, Testing, and Safety
- Developing unit, scenario, and automated evaluation harnesses
- Implementing guardrails, content filtering, and PII handling
- Conducting red teaming and chaos experiments for robustness
Summary and Future 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 Specialists for AI
- ML Architects responsible for production LangGraph systems
