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Course Outline
Advanced LangGraph Architecture for Government
- Graph Topology Patterns: nodes, edges, routers, subgraphs
- State Modeling: channels, message passing, persistence
- DAG vs Cyclic Flows and Hierarchical Composition
Performance and Optimization for Government
- Parallelism and Concurrency Patterns in Python
- Caching, Batching, Tool Calling, and Streaming
- Cost Controls and Token Budgeting Strategies
Reliability Engineering for Government
- Retries, Timeouts, Backoff, and Circuit Breaking
- Idempotency and Deduplication of Steps
- Checkpointing and Recovery Using Local or Cloud Stores
Debugging Complex Graphs for Government
- Step-Through Execution and Dry Runs
- State Inspection and Event Tracing
- Reproducing Production Issues with Seeds and Fixtures
Observability and Monitoring for Government
- Structured Logging and Distributed Tracing
- Operational Metrics: Latency, Reliability, Token Usage
- Dashboards, Alerts, and SLO Tracking
Deployment and Operations for Government
- Packaging Graphs as Services and Containers
- Configuration Management and Secrets Handling
- CI/CD Pipelines, Rollouts, and Canaries
Quality, Testing, and Safety for Government
- Unit, Scenario, and Automated Evaluation Harnesses
- Guardrails, Content Filtering, and PII Handling
- Red Teaming and Chaos Experiments for Robustness
Summary and Next Steps for Government
Requirements
- An understanding of Python and asynchronous programming for government applications
- Experience with LLM application development in a public sector context
- Familiarity with basic LangGraph or LangChain concepts as they apply to governmental systems
Audience
- AI platform engineers working for government agencies
- DevOps professionals focused on AI solutions for government
- ML architects managing production LangGraph systems in the public sector
35 Hours
