Anil Kumar
Anil Kumar
Associate Principal Engineer
Nagarro
Anil Kumar is an accomplished Quality Engineering professional with over 12 years of experience in functional, automation, and security testing. He has a proven track record of delivering zero-defect releases, improving process efficiency, and ensuring high-quality software delivery across complex systems.
Anil currently leads a team of QA engineers, guiding them in implementing best practices in test design, automation strategy, and defect prevention. His leadership fosters collaboration, innovation, and technical excellence throughout the testing lifecycle.
He specializes in test automation, continuous integration, and DevSecOps, with expertise in Jenkins, Maven, TestNG, Git, and Docker. His automation initiatives have significantly reduced manual effort, improved release velocity, and strengthened product reliability.
With a growing focus on cybersecurity, Vulnerability Assessment & Penetration Testing (VAPT), and process modeling through BPMN (Camunda), Anil continues to explore new frontiers in software quality and security.
He is deeply passionate about how AI-driven applications will be tested in the future, as artificial intelligence becomes integrated into every aspect of life. His forward-thinking mindset positions him at the intersection of quality, automation, and AI innovation, driving the evolution of intelligent testing frameworks.
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In the rapidly evolving world of Generative AI, simply prompting a language model is no longer enough. To harness the full potential of Large Language Models (LLMs), we need to make them aware of the broader ecosystem they operate in. This is where Model Context Protocol (MCP) steps in—acting as a bridge between models and external tools or systems to deliver more grounded, intelligent, and context-aware responses.
As LLM-based systems become mainstream, MCP will be a key enabler of scalable, modular, and intelligent apps. Testing MCP servers is not just about APIs and tools — it’s about ensuring the brain of your AI system thinks clearly, contextually, and correctly.
Example:By creating an MCP server for Selenium, we elevate it from a manual automation tool to an AI-controllable web agent. This opens new possibilities like:
• Building AI assistants that browse the web for users
• Creating intelligent test bots that understand and execute use cases
• Allowing non-technical users to drive automation through chat
So attend this session to know more about MCP- how its changing the way AI agents can interact with the end users.