Passion And Tech

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...