Building AI Agents with LangGraphin Glasgow

40 Hours/5 daysLive virtual trainingIntermediate

Classroom training and venue arrangements depend on the availability of a suitable conference hall in Glasgow, United Kingdom. Dates, seating capacity, facilities and the final venue are confirmed before booking.

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Explore Building AI Agents with LangGraph

Building AI Agents with LangGraph training in Glasgow, United Kingdom provides practical skills for developing intelligent, stateful, and tool-enabled AI agents. Learn LangGraph workflows, agent architecture, state management, tool calling, RAG integration, multi-agent systems, and human-in-the-loop patterns. This course helps developers and AI professionals build scalable Generative AI applications and production-ready AI agent solutions using Python, LangChain, and LangGraph.

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Learning from Glasgow

Glasgow offers a strong professional community, cultural heritage and accessible learning opportunities.

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City-page course benefits

Learn AI Agent Development in Glasgow, United Kingdom – Build practical expertise in designing intelligent AI agents using LangGraph, LangChain, Python, and modern Generative AI technologies.
Master LangGraph Workflows – Learn to create stateful, structured, and controllable AI agent workflows for real-world applications and enterprise automation.
Develop Tool-Enabled AI Agents – Understand how to connect AI agents with APIs, databases, search systems, and external tools to perform multi-step tasks.
Build RAG-Powered AI Agents – Integrate Retrieval-Augmented Generation (RAG), embeddings, vector databases, and knowledge retrieval into intelligent agent workflows.
Learn Multi-Agent Architecture – Understand how multiple specialized AI agents can collaborate to solve complex tasks and automate business processes.
Implement Human-in-the-Loop Workflows – Learn practical approaches for adding human review, approvals, intervention, and decision points to AI agent systems.
Strengthen Generative AI Development Skills – Gain hands-on knowledge of LLMs, prompt engineering, tool calling, state management, and agent orchestration in Glasgow, United Kingdom.
Build Production-Ready AI Applications – Apply LangGraph concepts to develop scalable, maintainable, and practical AI agent solutions for modern business requirements.
Work on Practical AI Agent Projects – Apply concepts through hands-on exercises and projects that demonstrate real-world AI agent development and workflow automation.
Prepare for AI Agent Career Opportunities – Develop job-relevant skills for roles such as AI Agent Developer, Generative AI Engineer, LLM Application Developer, and AI Solutions Engineer.
Learn Enterprise AI Automation – Discover how LangGraph-based agents can support customer service, knowledge management, workflow automation, research, and business operations.
Earn StepMerit Learning Course Recognition – Complete structured LangGraph training in Glasgow, United Kingdom and demonstrate your knowledge through assessment and practical learning activities.

Course curriculum

Module 1: Introduction to AI Agents and LangGraph
- Understanding Generative AI, LLMs, and AI agent fundamentals.
- Difference between traditional LLM applications, workflows, and autonomous agents.
- Introduction to LangGraph and its role in agent orchestration.
- Overview of building AI agents with LangGraph in Glasgow, United Kingdom.
Module 2: LangGraph Architecture and Core Concepts
- Understanding graphs, nodes, edges, and state.
- Designing stateful AI agent workflows.
- Managing transitions and execution paths.
- Understanding LangGraph application architecture.
Module 3: Environment Setup and LangGraph Development
- Setting up Python and the LangGraph development environment.
- Installing required libraries and dependencies.
- Configuring LLM providers and API credentials.
- Creating and running the first LangGraph application in Glasgow, United Kingdom.
Module 4: State Management in LangGraph
- Understanding state schemas and state updates.
- Managing information across agent workflow steps.
- Designing shared and persistent state.
- Handling structured data within agent workflows.
Module 5: Building Basic AI Agents
- Creating simple LLM-powered agents.
- Connecting models with prompts and application logic.
- Designing agent decision-making workflows.
- Building practical AI agent applications in Glasgow, United Kingdom.
Module 6: Tools and Function Calling
- Understanding tools and function calling.
- Connecting agents with APIs, databases, search systems, and external services.
- Designing tool-selection workflows.
- Handling tool execution and responses.
Module 7: Agent Routing and Conditional Workflows
- Implementing conditional edges and routing logic.
- Designing dynamic agent execution paths.
- Managing multiple workflow conditions.
- Building intelligent routing systems in Glasgow, United Kingdom.
Module 8: Retrieval-Augmented Generation (RAG) with LangGraph
- Understanding RAG architecture for AI agents.
- Integrating document loaders, embeddings, and vector databases.
- Implementing retrieval and contextual generation.
- Building RAG-powered AI agents.
Module 9: Multi-Agent Systems
- Understanding multi-agent architecture.
- Designing specialized agents for different tasks.
- Managing communication and coordination between agents.
- Building collaborative multi-agent workflows in Glasgow, United Kingdom.
Module 10: Human-in-the-Loop AI Workflows
- Understanding human oversight in AI agent systems.
- Implementing approval and intervention points.
- Managing human feedback within agent workflows.
- Designing controlled AI automation processes.
Module 11: Memory and Context Management
- Understanding short-term and long-term memory concepts.
- Managing conversational context across workflow steps.
- Designing context-aware AI agents.
- Optimizing memory and state management for practical applications.
Module 12: Error Handling and Workflow Reliability
- Identifying common AI agent workflow failures.
- Implementing error handling and recovery mechanisms.
- Managing failed tool calls and unexpected model outputs.
- Improving agent reliability for enterprise applications in Glasgow, United Kingdom.
Module 13: Advanced Agent Orchestration
- Designing complex agent workflows.
- Combining sequential, conditional, and parallel execution patterns.
- Managing multiple tools and agent capabilities.
- Building sophisticated AI orchestration solutions.
Module 14: LangGraph with Enterprise AI Applications
- Designing AI agents for enterprise use cases.
- Integrating business APIs and enterprise data sources.
- Building AI assistants for knowledge management and automation.
- Applying LangGraph to real-world business requirements in Glasgow, United Kingdom.
Module 15: Agent Evaluation, Monitoring, and Optimization
- Understanding AI agent evaluation techniques.
- Monitoring workflow execution and agent responses.
- Measuring accuracy, relevance, latency, and reliability.
- Optimizing LangGraph applications for production environments.
Module 16: Security and Responsible AI for Agents
- Understanding AI agent security considerations.
- Protecting sensitive data and API credentials.
- Managing permissions and tool access.
- Addressing prompt injection, data leakage, and responsible AI requirements.
Module 17: Deploying LangGraph AI Applications
- Preparing AI agents for deployment.
- Understanding APIs and application integration.
- Managing configuration and production environments.
- Deploying scalable AI agent applications in Glasgow, United Kingdom.
Module 18: Capstone Project – Production-Ready AI Agent
- Designing an end-to-end AI agent using LangGraph.
- Integrating LLMs, tools, workflows, state, and RAG.
- Implementing monitoring, error handling, and human-in-the-loop capabilities.
- Presenting and optimizing a practical AI agent solution for real-world use cases.

Exam details

Exam Level: Intermediate – AI Agent Development
Exam Type: Online computer-based examination consisting of multiple-choice questions and practical, scenario-based AI agent tasks.
Number of Questions: 40 questions
Exam Duration: 90 minutes
Passing Score: 70% (28 out of 40)
Exam Topics: LangGraph architecture, AI agent development, state management, nodes and edges, workflow orchestration, tool calling, function calling, RAG integration, multi-agent systems, human-in-the-loop workflows, memory, error handling, agent evaluation, security, and deployment.
Practical Assessment: Candidates may be required to analyze agent workflows, configure LangGraph components, implement tool-enabled workflows, and solve practical AI agent development scenarios.
Prerequisites: Basic to intermediate Python, familiarity with LLMs and Generative AI, and an understanding of APIs and AI agent concepts are recommended.
Exam Delivery: Online assessment accessible to learners in Glasgow, United Kingdom.
Certification: Successful candidates receive a StepMerit Learning Course Completion Certificate demonstrating foundational knowledge of AI agent development with LangGraph.

Plan your learning

Who this course is for

AI and Generative AI Developers – Professionals who want to build intelligent AI agents and LLM-powered applications using LangGraph in Glasgow, United Kingdom. Python Developers – Developers looking to apply their Python programming skills to AI agent development, workflow orchestration, and Generative AI applications. Machine Learning Engineers – ML professionals seeking practical expertise in LLM-based agents, RAG, tool calling, and agentic AI architectures. LLM Application Developers – Developers interested in creating production-oriented applications using LLMs, LangChain, LangGraph, and external tools. AI Engineers – Professionals who want to design and implement scalable AI agent…

Before you begin

Python Programming: Basic to intermediate Python knowledge is recommended for developing and implementing AI agent workflows with LangGraph in Glasgow, United Kingdom. Generative AI Fundamentals: Familiarity with Generative AI concepts, LLMs, and common AI application architectures is helpful. Large Language Models (LLMs): Understanding of prompts, tokens, context windows, embeddings, inference, and LLM interactions is recommended. LangChain Fundamentals: Basic knowledge of LangChain concepts such as prompts, chains, tools, and model integration will help learners understand LangGraph workflows. API and JSON Knowledge: Basic understanding of REST APIs, JSON, function calling, and external…

Learning outcomes

Problem Statement – An airline manages complex customer-service and operational workflows involving flight schedules, booking information, passenger enquiries, baggage policies, cancellation and refund requests, seat availability, travel policies, and disruption-related information. Traditional chatbots may struggle with multi-step requests that require decision-making, information retrieval, tool usage, state management, and interaction with multiple systems. A simple LLM-based application may also produce inconsistent responses when a customer request requires several actions to be completed in sequence. The challenge is to build a reliable AI-agent workflow using LangGraph that can manage…

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Room facilities / classroom arrangements

General planning information. Availability is confirmed for your selected venue and batch.

Typical classroom arrangements — subject to confirmation for the selected batch: 1. Learning space and seating: A suitable room layout with seating and desk space for course discussions and exercises. 2. Presentation equipment: A display or projector for instructor-led explanations; equipment requirements are confirmed before delivery. 3. Internet and power: Connectivity and charging access where needed for the course’s digital resources or labs. 4. Collaboration tools: A whiteboard or flip chart for group activities, demonstrations and note-taking. 5. Room comfort: Appropriate lighting and ventilation, with breaks included in the agreed training schedule. 6. Access and learner needs: Accessibility, refreshments and any specific accommodation requirements should be discussed with the adviser in advance. This is general planning information. It does not confirm a particular venue, equipment package or included refreshments.

Course expertise

Professional & Training Experience: 5+ Years Course Training Experience: 150+ Hours of training focused on AI agents, LangGraph, agentic AI, workflow orchestration, LLM integration, state management, tool calling, conditional routing, and multi-step AI workflows. Candidates Trained: 200+ candidates, including AI professionals, software developers, Python developers, machine learning engineers, data professionals, IT professionals, and Generative AI enthusiasts. Training Delivery Experience: Experienced in instructor-led online and classroom training with practical demonstrations, coding exercises, hands-on labs, assignments, case studies, and project-based learning. LangGraph Expertise…

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Insights for Building AI Agents with LangGraph

Learning insight 01

Building AI Agents with LangGraph: Complete Guide to AI Agent Development, LLM Workflows, RAG, Tools & Automation

Generative AI is moving beyond simple question-and-answer applications toward intelligent systems that can plan tasks, use external tools, retrieve information, and execute multi-step workflows. AI agents are an important part of this evolution, enabling applications to use Large Language Models (LLMs) to make…

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