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Course Outline
Advanced LangGraph Architecture
- Graph topology patterns: nodes, edges, routers, and subgraphs
- State modeling: channels, message passing, and persistence mechanisms
- DAG versus cyclic flows and hierarchical composition strategies
Performance and Optimization
- Parallelism and concurrency patterns in Python environments
- Techniques for caching, batching, tool calling, and streaming
- Cost control measures and token budgeting strategies
Reliability Engineering
- Implementation of retries, timeouts, backoff, and circuit breaking
- Ensuring idempotency and deduplication of processing steps
- Checkpointing and recovery using local or cloud-based storage
Debugging Complex Graphs
- Step-through execution analysis and dry runs
- In-depth state inspection and event tracing
- Reproducing production issues using seeds and fixtures
Observability and Monitoring
- Structured logging and distributed tracing frameworks
- Operational metrics: latency, reliability, and token usage
- Dashboard creation, alerting systems, and SLO tracking
Deployment and Operations
- Packaging graphs as services and containerized units
- Configuration management and secure secrets handling
- CI/CD pipelines, staged rollouts, and canary releases
Quality, Testing, and Safety
- Unit testing, scenario testing, and automated evaluation harnesses
- Guardrails, content filtering, and PII data handling
- Red teaming and chaos experiments for system robustness
Summary and Next Steps
Requirements
- Solid grasp of Python and asynchronous programming patterns
- Practical experience in developing LLM-based applications
- Foundational knowledge of LangGraph or LangChain concepts
Target Audience
- AI platform engineers
- AI DevOps professionals
- ML architects responsible for production LangGraph systems
35 Hours