TechLynk Selenium | DevOps | GenAI | Cloud

Selenium Automation Testing with Java + AI (2026 Edition)

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This comprehensive guide bridges the gap between traditional Selenium automation and modern Generative AI engineering. Designed for QA engineers, test automation specialists, and software developers, the curriculum starts with a rock-solid foundation in Java programming and Selenium 4.x core concepts. As students progress, they dive into advanced architectural patterns like the Page Object Model (POM) and hybrid testing frameworks integrated with TestNG, Maven, and Extent Reports. What sets this course apart is its forward-looking integration of artificial intelligence into the test lifecycle. Learners will discover how to harness GPT APIs for automated script generation, implement self-healing mechanisms to repair broken locators dynamically, and leverage OpenAI Vision for automated visual testing. Additionally, the course emphasizes modern DevOps practices, teaching students how to run automated suites within Docker containers and deploy robust CI/CD pipelines via Jenkins and GitHub Actions. By the end of this program, QA professionals will not only master classic web automation but also emerge as AI-augmented automation engineers capable of dramatically reducing test maintenance overhead.

Agentic AI: Build AI Agents with LangGraph, CrewAI & MCP

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The "Agentic AI: Build AI Agents with LangGraph, CrewAI & MCP" course is designed for developers, software engineers, and AI enthusiasts who want to transition from basic prompt engineering to building sophisticated, autonomous AI agents. Guided by TechLynk, this comprehensive bootcamp dives deep into the architecture of multi-agent systems, equipping learners with the skills to orchestrate collaborative workflows using cutting-edge frameworks. You will master critical agentic design patterns, including planning, reasoning, memory retention, and tool usage. The curriculum covers LangGraph for stateful multi-agent coordination, CrewAI for role-based agent collaboration, and Anthropic's Model Context Protocol (MCP) to standardize external tool integration. Through practical, hands-on projects, students will build real-world systems such as autonomous research assistants, automated coding agents, and human-in-the-loop validation pipelines. By the end of this program, participants will know how to connect LLMs to databases, APIs, and the live web, transforming static models into dynamic, production-ready digital workers capable of solving complex enterprise challenges.