Develop AI Agents and Multi-Agent Systems for QA Practice using LangChain, LangGraph, and LLMs
β±οΈ Length: 3.2 total hours
β 5.00/5 rating
π₯ 249 students
π November 2025 update
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- Course Overview: Bridging Software Excellence and Generative AI This specialized program serves as a transformative bridge between traditional Quality Assurance methodologies and the cutting-edge realm of Generative AI. It moves significantly beyond basic automation by teaching students how to construct autonomous agents that can reason, plan, and execute complex testing scenarios without constant human intervention. Over the course of 3.2 hours of intensive content, the curriculum explores the architectural shift from linear testing pipelines to dynamic, stateful multi-agent systems. Students will learn how to design environments where AI agents act as virtual testers, bug hunters, and documentation specialists, ensuring that the software delivery lifecycle is faster and more resilient.
- Course Overview: The Agentic Revolution in SDET Roles The curriculum is specifically designed for the 2025 landscape, where AI agents are becoming the primary drivers of test execution rather than just simple assistants. By focusing on the concept of “Agentic QA,” the course provides a blueprint for building self-correcting systems that can browse applications, identify UI changes, and update their own internal logic. You will explore how to move from static Selenium or Playwright scripts to intelligent agents that understand the intent of a test case rather than just the selectors. This shift in perspective is crucial for any Software Development Engineer in Test (SDET) looking to maintain a competitive edge in an industry increasingly dominated by large language model orchestration.
- Requirements / Prerequisites: Technical Foundations To succeed in this course, students should possess a fundamental understanding of Python programming, particularly regarding asynchronous functions and data structures, as these are essential for managing concurrent agent activities. A basic familiarity with QA principles, such as the software testing lifecycle, regression testing, and bug reporting, is necessary to contextualize the AI solutions provided. You will also need an active environment capable of running Jupyter Notebooks or VS Code, along with API access to major LLM providers like OpenAI or Anthropic to execute the practical agentic workflows discussed in the modules.
- Requirements / Prerequisites: Environment and Logic Beyond coding skills, learners should have a basic grasp of API interactions and environment variable management, as the course involves connecting various cloud-based intelligence services. No prior experience with LangChain or LangGraph is required, but a “problem-solving” mindset is vital for debugging the non-deterministic outputs often produced by LLMs. Access to a command-line interface and the ability to install Python libraries via pip or conda is mandatory to follow along with the hands-on building of the multi-agent QA architecture.
- Skills Covered / Tools Used: LangChain and LangGraph Mastery A core focus of the course is the utilization of LangChain for modular AI component construction and LangGraph for managing complex, cyclic agent states. Unlike simple linear chains, you will learn to build graphs that allow agents to loop back, reflect on their work, and retry failed test steps, which is critical for robust QA. The course dives deep into State Management, teaching you how to maintain a persistent memory across multiple agents so that a “Security Agent” and a “UI Agent” can share information seamlessly within the same testing session.
- Skills Covered / Tools Used: LLM Orchestration and Tool Calling You will master the art of Function Calling and Tool Binding, enabling your AI agents to interact directly with external software like Jira, GitHub, or browser automation frameworks. The course covers Multi-Agent Collaboration patterns, such as the “Supervisor” pattern where one agent delegates tasks to specialized sub-agents, and the “Peer-to-Peer” pattern where agents collaborate horizontally. Furthermore, you will gain hands-on experience with Prompt Engineering for QA, specifically focused on reducing hallucinations and ensuring that the AI generates valid, executable test code and structured bug reports.
- Benefits / Outcomes: Autonomous Test Maintenance One of the primary benefits of completing this course is the ability to implement Self-Healing Test Suites. By leveraging LangGraph, you will be able to build systems that automatically detect when a web element has changed and suggest or apply fixes to the test code autonomously. This drastically reduces the manual effort required for test maintenance, allowing QA teams to focus on high-level strategy rather than fixing broken locators. You will emerge with the ability to create a “Testing Brain” that understands the application under test as a whole.
- Benefits / Outcomes: Enhanced Bug Detection and Reporting Graduates will be able to deploy Multi-Agent Bug Hunting systems that simulate diverse user personas, leading to the discovery of edge cases that human testers might overlook. By orchestrating multiple LLMs to critique each other’s work, the quality of bug reports is significantly enhanced, providing developers with clearer reproduction steps and potential root-cause analyses. This results in a higher “signal-to-noise” ratio in your automated testing, making the QA process a source of genuine business intelligence rather than just a checklist.
- PROS: Focused and Modern Curriculum The course offers a highly specialized deep dive into LangGraph, which is currently the gold standard for stateful AI agents, making the content extremely relevant for the 2025 market. Its short duration (3.2 hours) ensures that there is no filler, providing a high-density learning experience that respects the professional’s time. The 5.0 rating reflects the practical, project-based approach that allows students to see immediate results in their local development environments.
- CONS: Rapidly Evolving Ecosystem Because the field of AI agents is moving at an incredible pace, some specific library syntax or secondary tool integrations might change shortly after the latest update, requiring students to stay proactive in checking documentation for the newest version releases.
Learning Tracks: English,IT & Software,Other IT & Software
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