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Duration 35 hours
Course Outline
Advanced LangGraph Architecture
- Graph topology patterns: nodes, edges, routers, and subgraphs.
- State modeling techniques: channels, message passing, and persistence.
- DAG vs. cyclic flows and hierarchical composition strategies.
Performance and Optimization
- Parallelism and concurrency patterns in Python.
- Implementing caching, batching, tool calling, and streaming.
- Cost controls and token budgeting strategies.
Reliability Engineering
- Retries, timeouts, backoff mechanisms, 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 methodologies.
- 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.
- Dashboard configuration, 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 testing.
Summary and Next Steps
Requirements
- Proficiency in Python and asynchronous programming concepts.
- Practical experience in LLM application development.
- Foundational knowledge of LangGraph or LangChain principles.
Target Audience
- AI platform engineers.
- AI DevOps specialists.
- ML architects responsible for production LangGraph systems.
