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Duration 35 hours
Course Outline
Advanced LangGraph Architecture
- Exploring graph topology patterns: nodes, edges, routers, and subgraphs
- Modeling state through channels, message passing, and persistence
- Distinguishing between DAG and cyclic flows for hierarchical composition
Performance and Optimization
- Applying parallelism and concurrency patterns in Python
- Leveraging caching, batching, tool calling, and streaming
- Implementing cost controls and token budgeting strategies
Reliability Engineering
- Configuring retries, timeouts, backoff, and circuit breaking
- Ensuring idempotency and step deduplication
- Utilizing local or cloud stores for checkpointing and recovery
Debugging Complex Graphs
- Performing step-through execution and dry runs
- Inspecting state and tracing events
- Reproducing production issues using seeds and fixtures
Observability and Monitoring
- Implementing structured logging and distributed tracing
- Tracking operational metrics such as latency, reliability, and token usage
- Managing dashboards, alerts, and SLO tracking
Deployment and Operations
- Packaging graphs as services and containers
- Handling configuration management and secrets
- Establishing CI/CD pipelines, rollouts, and canary deployments
Quality, Testing, and Safety
- Building unit, scenario, and automated eval harnesses
- Implementing guardrails, content filtering, and PII handling
- Conducting red teaming and chaos experiments for robustness
Summary and Next Steps
Requirements
- A solid grasp of Python and asynchronous programming concepts
- Practical experience in developing LLM applications
- Familiarity with foundational LangGraph or LangChain principles
Target Audience
- AI platform engineers
- DevOps professionals specializing in AI
- ML architects responsible for production LangGraph systems
