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Course Outline
LangGraph and Agent Patterns: A Practical Primer for Government
- Comparing Graphs to Linear Chains: When and Why
- Agents, Tools, and Planner-Executor Loops
- Hello Workflow: A Minimal Agentic Graph
State, Memory, and Context Passing for Government
- Designing Graph State and Node Interfaces
- Short-Term Memory versus Persisted Memory
- Context Windows, Summarization, and Rehydration
Branching Logic and Control Flow for Government
- Conditional Routing and Multi-Path Decisions
- Retries, Timeouts, and Circuit Breakers
- Fallbacks, Dead-Ends, and Recovery Nodes
Tool Use and External Integrations for Government
- Function/Tool Calling from Nodes and Agents
- Consuming REST APIs and Databases from the Graph
- Structured Output Parsing and Validation
Retrieval-Augmented Agent Workflows for Government
- Document Ingestion and Chunking Strategies
- Embeddings and Vector Stores with ChromaDB
- Grounded Responses with Citations and Safeguards
Evaluation, Debugging, and Observability for Government
- Tracing Paths and Inspecting Node Interactions
- Golden Sets, Evaluations, and Regression Tests
- Quality, Safety, and Cost/Latency Monitoring
Packaging and Delivery for Government
- FastAPI Serving and Dependency Management
- Versioning Graphs and Rollback Strategies
- Operational Playbooks and Incident Response
Summary and Next Steps for Government
Requirements
- Demonstrated proficiency in Python
- Experience developing large language model (LLM) applications or prompt chains
- Familiarity with REST APIs and JSON data formats
Audience for Government
- Artificial intelligence engineers
- Product managers
- Developers working on interactive LLM-driven systems
14 Hours
