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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
- Contrasting DAGs with cyclic flows and hierarchical composition
Performance and Optimization
- Parallelism and concurrency patterns in Python
- Strategies for caching, batching, tool invocation, and streaming
- Cost management and token budgeting techniques
Reliability Engineering
- Implementing retries, timeouts, backoff, and circuit breaking
- Ensuring idempotency and step deduplication
- Checkpointing and recovery via local or cloud-based stores
Debugging Complex Graphs
- Step-through execution and dry-run simulations
- State inspection and event tracing
- Replicating production issues using seeds and fixtures
Observability and Monitoring
- Structured logging and distributed tracing
- Tracking operational metrics: latency, reliability, and token usage
- Configuring dashboards, alerts, and SLO monitoring
Deployment and Operations
- Packaging graphs into services and containers
- Managing configurations and handling secrets
- Setting up CI/CD pipelines, rollouts, and canary releases
Quality, Testing, and Safety
- Unit, scenario, and automated evaluation harnesses
- Implementing guardrails, content filtering, and PII management
- Conducting red-teaming and chaos experiments for robustness
Summary and Next Steps
Requirements
- A solid grasp of Python and asynchronous programming patterns
- Practical experience in developing LLM applications
- Basic knowledge of LangGraph or LangChain principles
Target Audience
- AI platform engineers
- DevOps professionals focused on AI
- ML architects responsible for production LangGraph systems
