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Course Outline
LangGraph and Agent Architectures: An Operational Overview
- Comparative analysis of graph-based versus linear chain structures: strategic application criteria
- Implementation of agents, integrated tools, and planner-executor operational cycles
- Foundational workflow construction: establishing a minimal agentic graph
State Management, Memory Architecture, and Context Transmission
- Architecting graph state schemas and node interface specifications
- Differentiation between transient and persistent memory storage mechanisms
- Management of context windows, data summarization, and state rehydration protocols
Conditional Logic and Process Control
- Dynamic routing and decision-making across multiple operational pathways
- Implementation of retry mechanisms, timeout thresholds, and circuit breaking
- Establishment of fallback procedures, termination points, and recovery nodes
External Tool Integration and API Consumption
- Invocation of functions and tools by nodes and autonomous agents
- Integration with external REST APIs and database systems via graph structures for government applications
- Standardization of structured output parsing and data validation
Retrieval-Augmented Generation (RAG) Agent Workflows
- Strategies for document ingestion and content chunking
- Implementation of vector embeddings and storage using ChromaDB
- Ensuring response accuracy through citation requirements and safety guardrails
Evaluation Frameworks, Debugging, and System Observability
- Traceability of execution paths and inspection of node interactions
- Utilization of validation sets, performance evaluations, and regression testing
- Monitoring of system quality, security compliance, and latency/cost metrics
Deployment and Operational Delivery
- Service delivery via FastAPI and management of system dependencies
- Graph version control and rollback strategies for risk mitigation
- Development of operational playbooks and incident response protocols
Summary and Strategic Next Steps
Requirements
- Practical proficiency in Python programming
- Demonstrated experience developing large language model applications or orchestrating prompt sequences
- Competence with RESTful application programming interfaces and JavaScript Object Notation (JSON) data formats
Target Audience
- Artificial intelligence engineers
- Product management personnel
- Software developers constructing interactive, large language model-integrated systems for government
14 Hours