Weekend batches
October 2026
09:00–15:00 · flexible hours
$799per learner
24 Oct 2026
4 of 25 seats available
31 Oct 2026
10 of 25 seats available
Generative AI
Building AI Agents with LangGraph in Saint Pierre and Miquelon
Building AI Agents with LangGraph training in Saint Pierre and Miquelon 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.
40 Hours/5 daysIntermediate4.6 · 1,000 reviews
Training calendar
Upcoming four-month course schedule
A new weekday and weekend batch starts every week. Each batch runs for 5 training days, based on the course duration of 40 Hours/5 days.
Weekend batches
November 2026
09:00–15:00 · flexible hours
$799per learner
07 Nov 2026
25 of 25 seats available
14 Nov 2026
25 of 25 seats available
21 Nov 2026
25 of 25 seats available
28 Nov 2026
25 of 25 seats available
Weekend batches
December 2026
09:00–15:00 · flexible hours
$799per learner
05 Dec 2026
25 of 25 seats available
12 Dec 2026
25 of 25 seats available
19 Dec 2026
25 of 25 seats available
26 Dec 2026
25 of 25 seats available
Weekend batches
January 2027
09:00–15:00 · flexible hours
$799per learner
02 Jan 2027
25 of 25 seats available
09 Jan 2027
25 of 25 seats available
16 Jan 2027
25 of 25 seats available
23 Jan 2027
25 of 25 seats available
30 Jan 2027
25 of 25 seats available
Course curriculum
What you’ll learn
40 Hours/5 daysIntermediate
- 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 Saint Pierre and Miquelon.
- 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 Saint Pierre and Miquelon.
- 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 Saint Pierre and Miquelon.
- 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 Saint Pierre and Miquelon.
- 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 Saint Pierre and Miquelon.
- 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 Saint Pierre and Miquelon.
- 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 Saint Pierre and Miquelon.
- 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 Saint Pierre and Miquelon.
- 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.
Entry requirements and prerequisites
- Python Programming: Basic to intermediate Python knowledge is recommended for developing and implementing AI agent workflows with LangGraph in Saint Pierre and Miquelon.
- 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 service integration is useful for building tool-enabled AI agents.
- AI Agent Concepts: Familiarity with AI agents, tools, workflows, decision-making, and function calling is beneficial but not mandatory.
- Development Environment: Learners should have a computer with Python and a suitable development environment for hands-on LangGraph projects in Saint Pierre and Miquelon.
Career value
Reasons to choose this course
Learn Modern AI Agent Development
Build practical skills in LangGraph, AI agents, and Generative AI technologies in Saint Pierre and Miquelon.
Master Stateful Agent Workflows
Learn how to design structured, stateful workflows for intelligent AI applications in Saint Pierre and Miquelon.
Hands-On LangGraph Practice
Apply concepts through practical exercises covering agent orchestration, tools, workflows, and state management.
Build Tool-Calling AI Agents
Learn to connect AI agents with APIs, databases, search tools, and external services for automated tasks.
Develop RAG-Based Agents
Integrate Retrieval-Augmented Generation, embeddings, and vector databases into AI agent applications in Saint Pierre and Miquelon.
Explore Multi-Agent Systems
Understand how multiple specialized agents can collaborate to handle complex tasks and business workflows.
Advantages
Turn learning into practical capability
Design Reliable Agent Architectures
Learn architectural patterns for creating maintainable AI agent applications with clearly defined states, transitions, and workflow logic.
Improve AI Workflow Control
Understand how controlled execution paths can make complex LLM-powered applications more predictable and easier to manage in Saint Pierre and Miquelon.
Integrate Multiple AI Components
Connect language models, tools, databases, APIs, retrieval systems, and application services into cohesive AI solutions.
Optimize Complex Task Execution
Learn how structured agent workflows can break sophisticated business requirements into manageable steps for more efficient AI application development.
Support Scalable AI Solutions
Develop an understanding of architectures that can be extended as agent capabilities, tools, data sources, and business requirements grow.
Gain Future-Ready Generative AI Expertise
Build specialized LangGraph knowledge that complements LLM and AI application development skills for emerging enterprise opportunities in Saint Pierre and Miquelon.
Exam format & assessment
- 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 Saint Pierre and Miquelon.
- Certification: Successful candidates receive a StepMerit Learning Course Completion Certificate demonstrating foundational knowledge of AI agent development with LangGraph.
Bring your learning together
Course conclusion
Building AI Agents with LangGraph training in Saint Pierre and Miquelon provides practical knowledge for developing intelligent, stateful, and scalable Generative AI applications. Learners gain hands-on experience with LangGraph workflows, LLM integration, tool calling, RAG, multi-agent systems, memory, human-in-the-loop patterns, and AI agent orchestration. The course helps developers and AI professionals strengthen their AI agent development, LangChain, LangGraph, and Generative AI skills and apply them to real-world enterprise automation and intelligent application use cases.
Target audience
- AI and Generative AI Developers
- Python Developers
- Machine Learning Engineers
- LLM Application Developers
- AI Engineers
- Software Developers
- RAG Developers and Engineers
- AI Automation Professionals
- AI Solutions Architects
- Technology and IT Professionals
- AI Consultants and Technical Leads
- AI Enthusiasts and Career Switchers
Verified learning evidence
Expertise, recognition and learner outcomes
Course-specific 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: Practical experience with LangGraph concepts including graphs, nodes, edges, state management, conditional routing, workflow transitions, persistence, and human-in-the-loop workflows.
- AI Agent Development: Hands-on experience designing AI agents capable of analysing requests, making decisions, calling tools, processing information, maintaining state, and completing multi-step tasks.
- Agentic AI Architecture: Experience developing structured agent workflows that combine LLMs, tools, business logic, external data sources, and decision-making processes.
- LLM Integration: Practical experience integrating Large Language Models with LangGraph workflows to develop contextual, interactive, and task-oriented AI applications.
- Tool & API Integration: Experience connecting AI agents with APIs, databases, search systems, Python utilities, external services, and enterprise tools.
- Airline Use Cases: Training and project experience applying AI-agent concepts to airline customer-service scenarios such as flight-status enquiries, booking assistance, baggage information, travel policies, flight disruptions, and customer-support workflows.
- Multi-Step Workflow Development: Experience designing sequential and conditional workflows where agents process intermediate information, use appropriate tools, maintain state, and determine the next action.
- Human-in-the-Loop Implementation: Practical experience incorporating human approval and intervention into AI-agent workflows for sensitive, high-impact, or exception-based operations.
- Agent Evaluation & Optimization: Experience testing AI-agent workflows for routing accuracy, tool selection, task completion, response quality, state management, reliability, and overall workflow performance.
- Security & Responsible AI: Training experience covering data privacy, access control, secure tool usage, sensitive information protection, hallucination management, human oversight, and responsible AI-agent implementation.
- Hands-On Project Guidance: Experience guiding learners through end-to-end AI-agent projects involving LangGraph workflows, state management, tool integration, conditional routing, LLMs, error handling, testing, and deployment concepts.
- Assessment Experience: Experienced in evaluating learners through coding assignments, practical labs, quizzes, scenario-based activities, AI-agent implementation tasks, workflow exercises, and project demonstrations.
Documented learner 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 state, execute multiple steps, use airline tools, make decisions, and provide consistent customer support while maintaining accuracy, security, and human oversight.
- How We Overcame the Problem – Learners analysed airline customer-service requirements and designed a stateful AI-agent architecture using LangGraph. They defined states, nodes, edges, tools, decision points, and workflow transitions for different airline operations. Learners implemented conditional routing, state management, tool calling, flight-information retrieval, booking-support workflows, response generation, and human approval for sensitive operations. The workflows were tested with different passenger scenarios and refined to improve accuracy, consistency, reliability, and task completion.
- Overall Implementation – Learners implemented an airline AI-agent workflow covering customer request → request classification → state initialization → information retrieval → agent decision → tool selection → data processing → conditional routing → action execution → response generation → validation → human review where required → final customer response.
- Passenger Request Analysis – Learners designed an AI agent capable of analysing passenger requests such as flight-status enquiries, booking questions, baggage information, seat requests, cancellations, refunds, and travel-policy questions.
- State Management – Learners implemented LangGraph state management to maintain relevant passenger information, request details, retrieved flight information, previous actions, and intermediate results throughout multi-step interactions.
- Agent Workflow Design – Learners created graph-based workflows using nodes and edges to represent airline tasks, processing stages, decisions, and transitions between different customer-service operations.
- Flight Information Retrieval – Learners integrated approved airline information sources and retrieval mechanisms to obtain relevant flight schedules, flight-status information, airport details, travel policies, and other authorised information.
- Tool and API Integration – Learners connected AI agents with approved airline tools, APIs, databases, booking systems, flight-status services, and other utilities to support specific tasks within the workflow.
- Booking Assistance – Learners developed an agent workflow that could assist customers with authorised booking-related enquiries by retrieving relevant information and guiding customers through appropriate next steps.
- Baggage and Travel Policy Assistance – Learners designed workflows for retrieving baggage allowances, travel requirements, cancellation policies, and other airline policies to provide contextual responses to passenger questions.
- Conditional Routing – Learners implemented decision-based routing so that different passenger requests could follow appropriate workflows based on request type, available information, required tools, business rules, or approval requirements.
- Multi-Step Agent Execution – Learners developed workflows where an AI agent could perform multiple sequential tasks, retain relevant state, process intermediate results, call appropriate tools, and continue execution until the passenger request was completed or escalated.
- Flight Disruption Support – Learners designed workflows for handling scenarios such as delays, cancellations, schedule changes, and missed connections by retrieving relevant information and guiding passengers toward appropriate support options.
- Human-in-the-Loop Workflow – Learners incorporated human approval points for sensitive actions such as cancellations, refunds, booking modifications, or other operations requiring authorised airline staff intervention.
- Error Handling and Workflow Recovery – Learners implemented mechanisms for handling missing flight information, unsuccessful API calls, invalid passenger details, unavailable services, and workflow exceptions to improve agent reliability.
- Agent Evaluation and Optimization – Learners tested AI-agent workflows using different airline scenarios and evaluated request classification, state management, tool selection, routing accuracy, task completion, response quality, and overall workflow performance.
- Airline Security and Responsible AI – Learners addressed risks including unauthorised access to passenger information, exposure of booking details, incorrect flight information, inappropriate automated actions, data privacy concerns, and the need for human oversight for sensitive operations.
- Evidence of Learning – Learners produced LangGraph workflow diagrams, state definitions, nodes and edges, agent configurations, airline tool integrations, API workflows, conditional-routing examples, flight-information retrieval workflows, booking-support scenarios, execution traces, test cases, error-handling strategies, evaluation results, and project documentation as evidence of their practical AI-agent development capabilities.
- Overall Learner Outcome – By completing the implementation, learners demonstrated the ability to design stateful AI-agent workflows using LangGraph, define nodes and transitions, manage conversation and workflow state, implement conditional routing, integrate airline tools and APIs, develop multi-step agent execution, handle flight and booking-related scenarios, incorporate human-in-the-loop validation, manage workflow errors, evaluate agent performance, and build responsible AI-agent solutions for realistic airline customer-service applications.
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Frequently asked questions
It is a practical course that teaches AI agent development, LangGraph workflows, LLM integration, tool calling, RAG, and multi-agent systems in Saint Pierre and Miquelon.
Python developers, AI engineers, ML professionals, LLM developers, and technology professionals can benefit from this course in Saint Pierre and Miquelon.
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