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Duration 35 hours
Course Outline
Advanced LangGraph Architecture
- Graph topology patterns: including nodes, edges, routers, and subgraphs
- State modeling: covering channels, message passing, and persistence
- DAG versus cyclic flows and hierarchical composition techniques
Performance and Optimization
- Parallelism and concurrency patterns in Python
- Strategies for caching, batching, tool calling, and streaming
- Cost controls and token budgeting strategies
Reliability Engineering
- Implementation of retries, timeouts, backoff, and circuit breaking
- Ensuring idempotency and deduplication of steps
- Checkpointing and recovery using local or cloud stores
Debugging Complex Graphs
- Step-through execution and dry run techniques
- State inspection and event tracing methods
- Reproducing production issues using seeds and fixtures
Observability and Monitoring
- Structured logging and distributed tracing
- Operational metrics: including latency, reliability, and token usage
- Dashboards, alerts, and SLO tracking
Deployment and Operations
- Packaging graphs as services and containers
- Configuration management and secrets handling
- CI/CD pipelines, rollouts, and canary releases
Quality, Testing, and Safety
- Unit, scenario, and automated evaluation harnesses
- Guardrails, content filtering, and PII handling
- Red teaming and chaos experiments for robustness
Summary and Next Steps
Requirements
- Understanding of Python and asynchronous programming patterns
- 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
