1. AI Is Transforming Software—and the Way We Code
This week, I’ve been struck by how much Agentic AI is changing the software development landscape. The old way of working—long product roadmaps, feature-stuffed launches, and hard-coded business logic—is fading fast. AI has accelerated timelines dramatically. What once took months now needs to be shipped in days or weeks, or someone else will launch it first.
Even more profound is how AI is redefining business logic. Instead of embedding every decision in code, we’re now building prompt-driven wrappers that let AI agents dynamically choose execution paths based on user needs. This makes the system more adaptable and responsive—and more aligned with what customers actually want.
It’s also future-proof. Because logic is detached from the codebase and instead expressed through prompts, you can upgrade functionality simply by using better models. You’re not rewriting software—you’re swapping in better reasoning. That’s a huge shift.
What excites me most is how open-source tooling is catching up to support this new paradigm. One standout is Strands Agents, an SDK introduced by AWS to help developers build AI-native agent workflows. It abstracts away low-level orchestration so you can focus on defining high-level goals, constraints, and interactions with models like those hosted on Amazon Bedrock. Here is a workflow of how building an AI Agent with Strands works on AWS:
With tools like this, AI developers are becoming less like traditional coders and more like behavioral architects—designing how agents interact, how context flows, and how outcomes are evaluated. It’s an entirely different craft, and we’re just beginning to explore its creative potential.
📦 Definition: What Is Agentic AI?
Agentic AI refers to artificial intelligence systems that can operate autonomously over extended periods, managing multi-step tasks, maintaining context, and making decisions dynamically. Unlike traditional AI, which waits for input and responds reactively, agentic AI takes initiative, orchestrates tools, and adapts in real-time—making it ideal for complex workflows like software development, research, and operations.
2. Claude Opus 4 vs. Earlier Models: A New Level of Autonomy
Claude Opus 4 represents a massive leap from earlier versions and even Claude Sonnet 4. It supports advanced software development use cases with capabilities like:
- Running for up to 7 continuous hours
- Autonomously refactoring large codebases
- Tracking state, debugging, and generating unit tests end-to-end
Compared to Claude Sonnet, which is better suited for quick queries and lightweight tasks, Opus 4 is built for deep reasoning, memory, and workflow continuity—perfect for agentic coding setups.
This capability makes it a direct competitor to GPT-4.1, but with better long-form task execution and more reliable multi-step problem solving. For someone like me, building with Bedrock models every day, this makes Opus 4 a true co-developer.
3. Vibe Coding with Q CLI – My Daily Developer Experience
These days, I genuinely feel like Iron Man talking to JARVIS when I’m coding. Using tools like Q CLI, I’m no longer just typing instructions—I’m collaborating with a reasoning engine.
It’s a two-way dialogue between me and the AI, where I describe the outcome I want and it helps me figure out the best way to get there. This “vibe coding” approach—navigating, debugging, and iterating through conversation—feels natural, empowering, and incredibly fast. I’m coding through intuition, conversation, and guided reasoning.
Agentic tools like Strands and conversational dev flows like Q CLI are ushering in a new era where developers become orchestrators of intelligence, not just command writers.
4. Prompt Engineering Insight: Chain of Thought Prompting
One of the most useful prompt techniques I’ve adopted is Chain of Thought (CoT) prompting. It’s especially powerful for reasoning-heavy tasks like logic puzzles, math, or multi-step coding problems.
Based on Anthropic’s documentation, here are some key tips:
Key Tips:
- Ask the model to think step-by-step before responding
- Use prompts like “Let’s think this through” or “Step-by-step”
- Great for improving model accuracy, traceability, and course correction mid-task
Example: Q: How many hours are in 3 days?
A: Let’s think step-by-step. There are 24 hours in a day. So, 3 x 24 = 72. The answer is 72.
5. Personal Reflection
I love coding today.
I don’t regret for a second that I took a big step down from being a manager and a leader to becoming just another member on the team as a software developer. I love being hands-on. I love being connected to the technology—because that’s how I learn best.
Honestly, I don’t think I would’ve been able to keep up with all the AI progress and rapid trends if I hadn’t been working directly with the code every day. And I’m so grateful that I’ve had the chance to play with this technology firsthand.
It’s fun. It’s fascinating. And sometimes, I can’t even believe what I’m able to build now. It blows my mind.
My experiments with AI 
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