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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
- DAG versus cyclic flows and hierarchical composition
Performance and Optimization
- Parallelism and concurrency patterns within Python
- Techniques for caching, batching, tool invocation, and streaming
- Strategies for cost control and token budget management
Reliability Engineering
- Implementation of retries, timeouts, backoff, and circuit breaking
- Ensuring idempotency and deduplication of steps
- Checkpointing and recovery utilizing local or cloud-based stores
Debugging Complex Graphs
- Step-through execution and dry run simulations
- Inspection of state and detailed event tracing
- Reproducing production issues using seeds and fixtures
Observability and Monitoring
- Structured logging and distributed tracing
- Tracking operational metrics: latency, reliability, and token usage
- Management of dashboards, alerts, and SLO compliance
Deployment and Operations
- Packaging graphs as standalone services and containers
- Configuration management and secure handling of secrets
- CI/CD pipelines, staged rollouts, and canary deployments
Quality, Testing, and Safety
- Development of unit, scenario, and automated evaluation harnesses
- Implementation of guardrails, content filtering, and PII management
- Red teaming and chaos experiments to ensure system robustness
Summary and Next Steps
Requirements
- Proficiency in Python and asynchronous programming concepts
- Practical experience in developing LLM applications
- Working knowledge of fundamental LangGraph or LangChain concepts
Target Audience
- AI platform engineers
- DevOps professionals specializing in AI
- ML architects responsible for production LangGraph systems
