LangGraph for Marketing Automation Training Course

LangGraph is a framework designed for creating stateful, multi-actor LLM applications as composable graphs, featuring persistent state and execution control.

This instructor-led live training, available online or onsite, targets advanced AI platform engineers, AI DevOps specialists, and ML architects looking to optimize, debug, monitor, and operate production-grade LangGraph systems.

Upon completing this training, participants will be able to:

• Design and optimize complex LangGraph topologies to enhance speed, reduce costs, and improve scalability.
• Build reliability into systems using retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
• Debug and trace graph executions, inspect states, and systematically reproduce issues encountered in production.
• Instrument graphs with logs, metrics, and traces, deploy them to production, and monitor SLAs and associated costs.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation in a live-lab environment.

Customization Options

• For customized training requests, please contact us to arrange your session.

LangGraph is a framework designed for constructing stateful, multi-agent LLM applications by composing graphs that maintain persistent state and allow precise control over execution flow.

This instructor-led training, available both online and on-site, is tailored for intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based financial solutions with robust governance, observability, and compliance.

Upon completion of this training, participants will be equipped to:

• Design financial LangGraph workflows that align with regulatory and audit requirements.
• Integrate financial data standards and ontologies into graph state and tooling.
• Implement reliability, safety, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems for performance, cost, and SLAs.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a framework for constructing LLM applications with a graph structure, supporting planning, branching, tool use, memory, and controllable execution.

This instructor-led, live training (available online or onsite) is designed for beginner-level developers, prompt engineers, and data practitioners aiming to design and build reliable, multi-step LLM workflows using LangGraph.

By the end of this training, participants will be able to:

• Explain core LangGraph concepts (nodes, edges, state) and when to use them.
• Build prompt chains that branch, call tools, and maintain memory.
• Integrate retrieval and external APIs into graph workflows.
• Test, debug, and evaluate LangGraph apps for reliability and safety.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises on design, testing, and evaluation.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph facilitates stateful, multi-actor workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, enabling interoperability, and developing decision-support systems that integrate seamlessly with medical workflows.

This instructor-led, live training session (available online or onsite) targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

Upon completion of this training, participants will be able to:

• Design healthcare-specific LangGraph workflows with a focus on compliance and auditability.
• Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD).
• Apply best practices for reliability, traceability, and explainability in sensitive environments.
• Deploy, monitor, and validate LangGraph applications within healthcare production settings.

Format of the Course

• Interactive lectures and discussions.
• Hands-on exercises using real-world case studies.
• Implementation practice in a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

LangGraph serves as a framework for constructing stateful, multi-agent LLM applications as composable graphs, featuring persistent state and precise execution control.

This instructor-led, live training (available online or onsite) targets intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based legal solutions with the necessary compliance, traceability, and governance controls.

Upon completing this training, participants will be capable of:

• Designing legal-specific LangGraph workflows that ensure auditability and compliance.
• Integrating legal ontologies and document standards into graph state and processing.
• Implementing guardrails, human-in-the-loop approvals, and traceable decision paths.
• Deploying, monitoring, and maintaining LangGraph services in production with observability and cost controls.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• Hands-on implementation in a live-lab environment.

Customization Options

• To request customized training for this course, please contact us to arrange.

LangGraph serves as a framework designed for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.

This instructor-led, live training (available online or onsite) targets intermediate-level engineers and product teams who wish to combine LangGraph’s graph logic with LLM agent loops to build dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.

By the end of this training, participants will be able to:

• Design graph-based workflows that coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallbacks for robust execution.
• Integrate retrieval, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and harden agent behavior for reliability and safety.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based design exercises and peer reviews.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.