Curated Engineering Insights

Today's users expect more from digital platforms. They want experiences that feel smart and understand what they need. This expectation is particularly acute in a wide range of platforms like; web applications, mobile apps, SaaS, etc., where complex workflows and feature-rich dashboards can easily overwhelm users. 

In the early days of AI, a lot of focus was on prompt engineering; writing short, smart instructions to get the right answers from language models. This worked well for simple tasks.  However, as we move into the era of Agentic AI solutions, where systems act with autonomy and reasoning, prompt engineering alone isn’t enough. What’s needed now is a structured approach to designing workflows, data flows, and decision logic.

The shift from automation to autonomy is happening quickly, and the frameworks you choose today will define how your systems think, adapt, and scale tomorrow. 

With Gartner forecasting that agentic AI will be embedded in 40% of applications by 2026, up from under 5% in 2023, it is clear that agentic intelligence is moving from experimental to essential. The question isn’t if you need to use agentic AI but how soon and how effectively you can deploy it in your systems.

You’re surrounded by data like sales numbers, supply chain updates, and performance metrics, all streaming in from different fields in your system. This data holds powerful insights, but when it’s scattered across departments and platforms, it becomes hard to connect the dots and make faster, confident decisions.

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.