Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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
- Cost controls and token budgeting strategies
Reliability Engineering
- Retries, timeouts, backoff, 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 tests, scenario tests, and automated evaluation harnesses
- Guardrails, content filtering, and PII handling
- Red teaming and chaos experiments for robustness
Summary and Next Steps
Requirements
- Understanding of Python and asynchronous programming
- Experience in developing LLM applications
- Familiarity with basic LangGraph or LangChain concepts
Audience
- AI platform engineers
- DevOps for AI
- ML architects managing production LangGraph systems
35 Hours