By: Krishna Tammana, CTO, Gupshup
There is an inherent contradiction in how we interact with AI: we simultaneously expect it to be predictable enough to be reliable, yet unpredictable enough to be useful in novel situations. This tension becomes even more pronounced in business conversations, where the goal is to foster meaningful customer connections at scale. For businesses trying to connect with customers, this balance is especially delicate. Bridging this gap calls for AI to go beyond automated responses to truly understand customer needs and adapt on the go, replicating the essence of human interaction.
Enter Agentic AI – systems that can independently pursue goals, adapt to new situations, and make complex decisions – shows us a new way forward. While some view it simply as AI that can “act on its own,” the real story lies in how these systems are bringing a human touch to digital conversations.
Perhaps the most fascinating aspect of Agentic AI is how it evolves its contextual awareness. Today, we are seeing AI assistants who respond to what is said while understanding the deeper meaning behind customer interactions. For instance, when a customer inquires about a specific product, the AI can analyse that customer’s purchase history and browsing behaviour to proactively suggest complementary items or accessories relevant to the customer’s tastes and buying habits. Now, when this contextual awareness pairs with what we might call “conversational intuition,” in business settings, this shows up as AI systems that naturally switch between professional formality and empathetic engagement. These systems respond, anticipate, adapt, and shape the flow of communication in ways that redefine the understanding of what constitutes a “conversation.”
If you look at WhatsApp, its business platform demonstrates this evolution perfectly. What started as simple automated messaging has transformed into sophisticated AI agents that manage entire customer journeys across channels, evolving from basic chatbots to intelligent systems that can handle complex interactions, personalised responses, and maintain continuous engagement across multiple touchpoints. When a customer casually brings up their previous purchase while browsing products on Instagram, the AI intelligently incorporates this information into the conversation. When they later switch to WhatsApp to complete their purchase, the AI remembers that casual reference and uses it to guide the conversation. It is interesting how these agents now seamlessly transition conversations between WhatsApp, Instagram DMs, and SMS while maintaining perfect context – much like a skilled human agent would remember your previous conversations regardless of where they occurred. When a customer begins browsing products via Instagram DMs but prefers to complete their purchase through WhatsApp, the AI maintains the full conversational context and relationship history across this journey.
Each interaction adds to the AI’s understanding, creating a conversation network, which is, in essence, a web of interconnected insights that emerge from millions of individual exchanges. This is more about understanding customer needs with the kind of intuition that once belonged exclusively to your most experienced customer service representatives; the subtle threads that make up each customer relationship.
It is intriguing how these systems remember what was said, how it was said, and in what circumstances. An AI agent might notice that a customer prefers detailed technical information in the morning but responds better to quick, simple answers during evening conversations. This kind of adaptive awareness creates a more natural flow of communication.
Today, we are moving beyond simple customer satisfaction metrics to something more nuanced – the quality of each interaction. Traditional approaches always faced a tough choice: you could either scale up your customer service or keep it personal, but not both. Agentic AI is dissolving this dichotomy. By combining contextual awareness with conversational intuition, these systems create an intimacy of sorts to maintain deeply personalised interactions across massive customer bases.
Looking ahead, we are seeing new possibilities emerge. Businesses are now able to offer the kind of attentive, personalised service that was once limited to luxury brands or small local shops, but at a scale that serves millions of customers.
This shift goes beyond handling more customer queries or reducing response times. We are seeing the emergence of business capabilities that were not possible before – predictive engagement that anticipates customer needs, emotional intelligence that guides conversations naturally, and relationship management that grows more nuanced with each interaction.
The future is not about AI replacing human communication – it is about enhancing and facilitating it. As these systems demonstrate deeper contextual awareness and more natural conversational abilities, they are creating new ways for businesses to build lasting, more meaningful customer relationships. The result? Genuine and meaningful connections at a scale that was unimaginable just a few years ago.