Software Engineering Insights

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? 

The e-commerce industry is entering a new era, driven by the rise of agentic AI systems capable of making decisions and taking actions independently. Unlike traditional AI tools that require constant human input, agentic AI in e-commerce understands context, plans, and executes complex tasks across multiple touchpoints.

Voicebots are becoming an essential tool for businesses looking to improve customer interactions and streamline their services. These AI-driven assistants use natural language processing to understand and respond to voice commands, making conversations feel more human and intuitive. As technology advances, voicebots are no longer just a novelty - they are now widely used across industries for tasks such as customer support, virtual assistance, automated booking and so on.

According to Market Research Future, the voicebot market was valued at USD 7.97 billion in 2024 and is projected to reach USD 54.64 billion by 2034, growing at a CAGR of 22.51%.

Gen Z’s shopping habits are driving a major shift in retail, and for tech leaders, the focus is on how to meet their expectations for speed and personalization. AI in visual search is quickly becoming the preferred tool with the majority of Gen Z favoring it over text-based searches. They want quick results, where a single photo can lead them directly to the products they’re after. AI-powered styling takes it a step further by analyzing personal preferences and delivering real-time suggestions that align with each shopper’s unique taste. These tools don’t just cater to Gen Z’s desire for instant gratification - they create new opportunities for brands to understand and engage with their audience more deeply. 

AI has become the backbone of innovation across industries, but not all AI systems are created with full transparency. Black Box AI is one of the most talked-about and often controversial concepts in this space but what is Black Box AI? 

Black Box AI refers to AI models whose internal workings are either too complex to understand or are hidden, which makes it difficult to explain how they arrive at specific outputs or decisions. Unlike traditional algorithms, where cause and effect can be clearly traced, Black Box AI operates behind a veil of complexity, often using deep learning models with millions of parameters.

Artificial Intelligence has made remarkable progress in processing visual and textual data separately, but real-world scenarios require a combined understanding of both. This is where Vision Language Models (VLMs) come into play. VLMs are AI systems designed to process and understand both visual and textual information simultaneously, enabling them to interpret images in context with language and vice versa.