Software Engineering Insights

It’s a situation many tech leaders eventually encounter: a business-critical application is running, but the source code is either missing, broken beyond comprehension, or too outdated to be useful. You’re left with an interface that works, but a backend that no one fully understands. No documentation. No clean code. No clear path forward.

What if you could still rebuild the entire application, its interface, functionality, and structure using nothing more than a screenshot or a screen recording?

Autonomous agents are no longer a research concept; they are being deployed to handle complex, multi-step workflows across tools, departments, and even entire business units. From automating software engineering tasks to accelerating product research or managing multi-channel operations, agentic AI frameworks are powering a new class of systems that can reason, plan, and take actions with minimal human intervention.

As AI becomes deeply embedded in everything from national security to healthcare and education, a new question is arising: Who really controls the artificial intelligence that is shaping our future? That’s where Sovereign AI enters the conversation. It’s not just a technical term; it is a growing movement among nations to build and own AI systems that reflect their values, protect their data, and reduce reliance on foreign tech giants. 

Artificial intelligence is stepping into a new phase, one where it doesn’t operate in isolation, but in collaboration. As real-world problems grow more complex and dynamic, there’s a growing shift toward AI systems that think, act, and adapt collaboratively. That’s where multi-agent AI systems come in: networks of intelligent agents that work together, often with competing or complementary goals to solve problems that a single AI simply can’t handle alone.

AI is no longer an abstract concept in healthcare, from accelerating diagnostics and optimizing treatment plans to enhancing operational workflows and patient engagement. Yet, not every AI innovation delivers equal value.

According to a report by Gitnux, the global AI‑in‑healthcare market is projected to grow from $45.2 billion in 2026 to $188 billion by 2030.

While some technologies are already reshaping clinical practices, others remain in experimental stages or are overhyped with minimal real-world impact. 

Some decisions in AI system design may seem minor at first glance, but can have a lasting impact; one such choice is between building a stateful or stateless AI agent. While the difference may seem technical on the surface, it has a strong influence on how your system responds, learns, and scales over time. Whether you're working on a simple query-response bot or a complex, context-aware assistant, the architecture you choose can shape the entire user experience. 

AI agents are becoming more capable and more unpredictable. From misinterpreting user intent to generating inaccurate or biased responses, even well-trained agents can behave inconsistently once deployed. Traditional QA approaches often fall short because AI agents don’t just process logic; they interpret language, context, and nuance. That’s why testing them requires more than checking for bugs; it demands evaluating behavior, adaptability, and failure modes. 

AI is becoming a central component in shaping e-commerce, not just through automation, but by enabling smarter and more personalized digital experiences.

According to SellersCommerce, the valuation of the global AI-enabled eCommerce market is projected to reach $8.65 billion by 2025 and grow to $22.60 billion by 2032, fueled by a 14.6% CAGR. 

Efficient customer support increasingly relies on intelligent systems capable of understanding and resolving issues without constant human oversight. AI agents are stepping into this role, managing routine inquiries and freeing human agents to focus on more complex tasks.

AI is no longer just a tool, it is becoming a decision-maker. While Generative AI has taken the spotlight by creating text, images, and even code at scale, a new contender is rising: Agentic AI. Unlike generative models that simply respond to prompts, agentic systems can take initiative, make decisions, and pursue goals with a degree of autonomy. So, what happens when AI stops just generating content and starts acting like an agent?