LangGraph Agent Workflows Training is designed to help developers and AI engineers build dynamic, stateful, and autonomous workflows using LangGraph and LLM agents. This course focuses on designing agent-based systems with graph-driven control flow, decision-making, memory, and tool integration. Participants will learn how to move beyond static chains to create flexible and reliable LLM agent workflows suitable for real-world applications.

• Build AI agent workflows using LangGraph.
• Design multi-step and stateful AI processes.
• Integrate tools, APIs, and LLMs into agent workflows.
• Optimize workflow automation and decision-making.
• Apply best practices for scalable AI agent development.

• AI engineers and LLM application developers
• Machine learning practitioners
• Software developers building agentic systems
• Technical architects and solution designers
• Developers exploring autonomous AI workflows

• Working knowledge of large language models and prompt engineering
• Familiarity with Python programming
• Experience with LangChain or similar frameworks is beneficial

• Ability to design dynamic, agent-based workflows using LangGraph
• Skills to implement decision-making and branching logic for LLM agents
• Understanding of agent memory, state, and context management
• Experience integrating tools and external APIs into agent workflows
• Best practices for building reliable and maintainable agent systems

By the end of this training, participants will be able to design, implement, and manage dynamic LLM agent workflows using LangGraph. Learners will gain practical skills to build flexible, reliable, and scalable agent-based AI systems suitable for production environments.

Introduction to LLM Agents and Dynamic Workflows
• Understanding agent-based AI systems
• Limitations of linear chains and static workflows
• Role of LangGraph in agent orchestration

LangGraph Architecture for Agent Workflows
• Graph nodes, edges, and state management
• Controlling execution flow for agents
• Designing dynamic and adaptive workflows

Building Core LLM Agents with LangGraph
• Agent roles, goals, and decision logic
• Tool usage and action selection
• Managing memory and context across steps

Implementing Conditional Logic and Branching
• Decision nodes and routing strategies
• Looping, retries, and fallback handling
• Error management in agent workflows

Advanced Agent Workflow Patterns
• Multi-agent coordination and collaboration
• Long-running and stateful agent processes
• Human-in-the-loop agent designs

Integrating Tools, APIs, and External Systems
• Connecting agents to tools and services
• Data retrieval and action execution
• Managing inputs, outputs, and side effects

Monitoring, Reliability, and Best Practices
• Observability and debugging agent workflows
• Reducing hallucinations and improving control
• Secure and ethical agent design considerations

Real-World Use Cases and Architectures
• Autonomous assistants and task agents
• Decision-support and reasoning agents
• Enterprise and production-ready agent systems

Hands-on Agent Workflow Implementation
• End-to-end LangGraph agent workflow build
• Real-world agent scenarios
• Participant practice with feedback

• Introduction to LangGraph Agents
• Multi-Agent and Workflow Design
• State and Memory Management
• Tool and API Integration
• Workflow Automation and Optimization
• Practical Agent Workflow Exercises

• Participation in hands-on agent workflow exercises
• Practical agent-based implementation assignments
• Scenario-driven technical assessment

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training and evaluation will receive an AcadNXT Certificate of Completion in LangGraph Agent Workflows Training, recognizing their expertise in building dynamic LLM agent workflows.