Software Engineering Insights

Have you observed a change in customer service with some of the large e-commerce stores lately? When we need help regarding an order, the customer service is handled by chatbots or AI instead of a human assistant. We even get a personalized recommendation on the website. When we want to return an order or report about it, an AI agent in retail service asks a few questions and resolves the issue right there in 30 seconds. 

The pace of the software development cycle today is relentless. In agile-driven organizations, releases happen every now and then, sometimes even multiple times a day. 

For years, we have relied on traditional testing: painstakingly written, updated, and maintained to ensure quality. But now, AI-powered testing agents have come to the rescue, and they are changing the game completely. 

The healthcare industry is walking a tightrope: driving innovation, enhancing patient outcomes, and streamlining operations, all under the weight of rising operational costs, workforce shortages, and ever-evolving healthcare regulations. In this high-stakes environment, AI in Electronic Health Records (EHRs) is emerging not as a mere technological enhancement, but as a strategic differentiator.            

For tech leaders, managing development cycles and unexpected bottlenecks can derail even the most carefully planned projects. Traditional project management tools often fall short in providing the real-time insights needed to address risks proactively. That’s where predictive project management, driven by AI agents, comes into play. These intelligent systems analyze historical data, real-time progress, team velocity, and resource allocation to forecast potential delays before they escalate. Instead of reacting to problems after the fact, tech leaders can now make informed decisions early, reallocating resources, adjusting timelines, or revising priorities with precision. 

In healthcare development, the challenge has shifted from simply digitizing medical records to designing systems that restore clinical focus.

A review published by a medical journal found that over 40% of healthcare professionals experience burnout,  linked directly to EHR use, with documentation burdens and after-hours data entry cited as major contributors. 

This isn’t a usability issue; it’s a signal that current EHR systems are out of sync with the reality of prompt healthcare delivery.

Here, AI shows the way.

Many companies struggle to make their AI initiatives work, despite investing heavily in high-end technologies and hiring top-tier talent, only to see no ROI delivered as expected. According to Gartner's prediction, 85% of AI implementations are likely to fail due to the absence of a solid data strategy.

Ever launched a product thinking everything was on point, only to have a bug slipping in that no one saw coming? You’re not alone. 

In fact, according to a recent research by Gitnux, 70% of bugs are reported post-release, highlighting limited test coverage.

Now, in 2025, the testing landscape has undergone major changes. Alongside tried and tested methods like manual testing, regression testing, and automated scripts, we’re seeing the rise of something new: AI agents. 

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.

Even after crossing the beta testing phase, QA teams find themselves stuck in long testing cycles, putting in tens of hours of manual effort before the software or a feature is ready for release. At this point, quality assurance becomes more than a checkpoint; it is the deciding factor between a successful launch that entices users and one that is forgotten.

According to a report by Gitnux, Enterprises using AI for testing have reduced their test script maintenance efforts by 65%, resulting in faster releases and more dependable software.