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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

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