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Course Outline
Advanced LangGraph Architecture
- Graph topology patterns: nodes, edges, routers, and subgraphs
- State modeling: channels, message passing, and persistence
- Comparing DAGs vs. cyclic flows and hierarchical composition
Performance and Optimization
- Parallelism and concurrency patterns in Python
- Caching, batching, tool calling, and streaming strategies
- Cost controls and token budgeting methodologies
Reliability Engineering
- Retries, timeouts, backoff, and circuit breaking
- Idempotency and step deduplication
- Checkpointing and recovery utilizing local or cloud stores
Debugging Complex Graphs
- Step-through execution and dry runs
- State inspection and event tracing
- Reproducing production issues using seeds and fixtures
Observability and Monitoring
- Structured logging and distributed tracing
- Operational metrics: 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
- A solid understanding of Python and asynchronous programming
- Practical experience in LLM application development
- Familiarity with fundamental LangGraph or LangChain concepts
Target Audience
- AI platform engineers
- DevOps professionals for AI
- ML architects managing production LangGraph systems
35 Hours
