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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 in Python.
- Caching, batching, tool calling, and streaming techniques.
- Cost controls and token budgeting strategies.
Reliability Engineering
- Retries, timeouts, backoff mechanisms, and circuit breaking.
- Idempotency and step deduplication.
- Checkpointing and recovery using local or cloud storage.
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 deployments.
Quality, Testing, and Safety
- Unit testing, scenario testing, and automated evaluation harnesses.
- Guardrails, content filtering, and PII handling.
- Red teaming and chaos engineering experiments for robustness.
Summary and Next Steps
Requirements
- Understanding of Python and asynchronous programming.
- Experience with LLM application development.
- Familiarity with core LangGraph or LangChain concepts.
Audience
- AI platform engineers.
- AI DevOps professionals.
- ML architects managing production LangGraph systems.
35 Hours
