Passion And Tech

How QA Engineers Can Thrive in the Generative AI Era by Lakshmi Narasimhan · Passion & Tech A few years ago, testing was all about discipline — writing test cases, running regressions, reporting bugs. Then came automation. Now, AI tools are writing tests, spotting issues, and suggesting fixes before we even log in. If you’re a QA or SDET, that’s a lot to process. Some of it feels exciting, some of it uncomfortable. But this isn’t the end of QA. It’s a new phase — one that rewards engineers who can mix curiosity with craft, and who are willing to keep learning. The QA Landscape in 2025 India still anchors the global QA workforce — roughly 2–3 million professionals across IT services and product companies. Manual testing is shrinking fast. Automation-heavy roles earn significantly more. Companies are quietly reshaping teams — fewer routine QA roles, more AI testing, performance, and security roles. The trend is clear: QA is shifting toward quality engin...

My last article explored the GenAI shift across engineering disciplines. This one focuses on the backend engineer—the architect at the heart of every modern system—and how their world is moving into the age of Agentic AI. If you have spent years designing APIs, building services, and orchestrating data, the transition is closer than you might think. Your deep understanding of systems is the critical foundation. From Passive APIs to Autonomous Agents For decades, backend engineering has established the core digital infrastructure: APIs, queues, databases, and business logic. These were passive systems, waiting for deterministic instructions. In the GenAI era, this role changes fundamentally. Instead of waiting for human instructions, these components are becoming intelligent agents that can reason, act, and coordinate autonomously. Consider the shift: Before: Your service handled a static request like /get_user_profile . Now: Your agent proactively reasons about the next best action...

The Talent Gap Myth Generative AI has created a perception that an entirely new set of roles is emerging — “prompt engineers,” “AI whisperers,” “autonomous-agent designers.” That narrative, while exciting, is incomplete. In reality, the foundations of GenAI systems are still deeply rooted in software engineering discipline . The roles are evolving, not disappearing. What changes is the interface: engineers now work with language and context instead of code and schema . The future won’t be built by replacing traditional engineers — it will be built by those who learn to translate their existing skills into this new paradigm of adaptive, context-aware systems. This article kicks off a series exploring how traditional software roles are evolving in the GenAI era. Over the next few posts, we’ll deep-dive into each transition — backend to context architecture, frontend to AI interaction design, QA to hallucination testing, and more. Backend Engineer → Context Architect / LLM Integrator Th...

There’s a lot of buzz around Generative AI, with many touting it as a new computing paradigm — but that misses the point. GenAI and Agentic AI are still software systems , built on the same engineering discipline that’s powered the IT industry for decades. Both follow principles of modularity, clear responsibility, orchestration, and observability. What’s changed isn’t the foundation — it’s the interface . Intelligence is now expressed through natural language instead of code, and coordination happens through context instead of strict APIs. The Core Hypothesis Both Traditional and Generative AI share the same architectural backbone: modular components with clear boundaries and predictable interactions. What’s evolved is the language of communication — from code contracts to context contracts. Deterministic logic has given way to probabilistic reasoning, but the need for disciplined engineering remains unchanged. Parallels Between Traditional Systems and Agentic AI a. Single...

Recently, I had an insightful discussion with a seasoned professional planning to transition back into a full-time role after nearly eight years dedicated to running his own startup. This conversation reaffirmed my belief that career planning is not only about setting aspirations but also about strategically positioning oneself for success.  Through my coaching experience on igotanoffer , I've learned that a structured, deliberate approach can make all the difference—whether you’re starting out, looking to grow, or planning a comeback after an entrepreneurial journey. The AAP Framework: Aspire, Assess, Position. To guide individuals through this process, I often recommend using the AAP Framework—Aspire, Assess, and Position. Here’s how it works: Aspire Define Your Goals: Clearly identify what you want to achieve in your career. This could be a specific role, industry, or level of responsibility. Visualize Success: Envision where you want to be in the future. Creating a vivid mental...

  In the previous post , I shared my update about building collaborative AI agents with the CrewAI framework. We explored the power of orchestrating AI agents to tackle complex tasks. However, that setup relied heavily on powerful Large Language Models (LLMs) accessible through expensive API tokens. Since then, I've discovered a transformative approach: running CrewAI agents entirely locally. This shift leverages tools like Ollama and open-source models such as DeepSeek . The method was brilliantly demonstrated by Tyler AI in his YouTube video "How to Build CrewAI Agents with Deepseek R1 100% Locally" . The Economic and Practical Advantages of Local LLMs As Tyler AI emphasizes, the financial contrast between API-driven models like OpenAI's gpt-3.5-turbo-016 (around $60 per million output tokens) and open-source alternatives like DeepSeek R1 (roughly $2.19 per million output tokens) is substantial. Impressively, DeepSeek R1 achieves performance comparable to or ...

When I first explored CrewAI, I was fascinated by its potential to create collaborative AI agents for real-world tasks. As someone interested in both AI and finance, I decided to build a team of AI-powered stock analysts to automate financial data analysis and investment recommendations. In this article, I’ll walk you through how I set up my first CrewAI agents—from defining their roles to executing market analysis tasks. Whether you're into AI automation or stock trading, this guide will help you get started on building intelligent financial assistants. Building your first CrewAI agents for analyzing stocks involves several key steps to set up a collaborative multi-agent AI system. Here's a step-by-step guide to help you through the process: 1. Set Up Your Development Environment Install Python : Ensure you have Python version 3.10 to 3.13 installed. You can download it from the official Python website . Install UV : This project uses UV for dependency management. Inst...

The Rise of AI Agents: Market Growth, Business Opportunities, and Industry Leaders AI-powered agents are no longer just futuristic concepts—they're transforming industries, automating workflows, and redefining business efficiency. As businesses increasingly seek AI-driven solutions, the market for AI agents is expanding at an unprecedented rate, creating lucrative opportunities for entrepreneurs, startups, and enterprises alike. The Booming Market for AI Agents The demand for AI agents is skyrocketing. Recent projections suggest: By 2030 , the market will grow from $5.1 billion (2024) to $47.1 billion , reflecting a 44.8% CAGR . By 2035 , estimates predict it could surge to $216.8 billion , with a 40.15% CAGR ( source ). This explosive growth is fueled by advancements in Large Language Models (LLMs) , workflow automation , and enterprise AI adoption . Companies across finance, healthcare, e-commerce, and SaaS are integrating AI agents to boost productivity and reduce operationa...