Building Dynamic Workflows with LangGraph and LLM Agents Training Course

LangGraph and Agentic Architectures: A Foundational Overview

• Distinguishing graph-based structures from linear execution chains: application and rationale
• Implementation of autonomous agents, utility tools, and planner-executor cycles
• Constructing a minimal agentic graph workflow

Data State, Memory Management, and Context Integration

• Structuring graph state and defining node interfaces
• Differentiating ephemeral memory from persistent storage solutions
• Managing context windows, text summarization, and state rehydration

Conditional Logic and Workflow Control

• Implementing dynamic routing and multi-path decision logic
• Configuring retry mechanisms, timeout thresholds, and circuit breaker protocols
• Establishing fallback procedures, error handling, and recovery nodes

External Tool Integration and Data Access

• Executing function and tool calls within nodes and agent frameworks
• Interfacing with REST APIs and database systems through graph operations
• Ensuring structured output parsing and data validation

Retrieval-Augmented Generation for Agent Workflows

• Strategies for document ingestion and data chunking
• Utilizing embeddings and vector databases, including ChromaDB
• Generating grounded responses with citation integrity and safety controls

Evaluation Frameworks, Debugging, and Observability

• Tracking execution paths and analyzing node interactions
• Developing baseline datasets, performance evaluations, and regression testing
• Monitoring system quality, security compliance, and latency/cost metrics

Deployment and Operational Delivery

• Managing service deployment via FastAPI and dependency requirements
• Implementing version control for graph architectures and rollback protocols
• Developing operational runbooks and incident response procedures

Summary and Strategic Next Steps