The mobile app revolution transformed how people interact with technology, placing a wide range of services at their fingertips. However, as digital experiences evolve, a new shift is on the horizon—Agentic AI. Agents are poised to replace traditional apps, offering users a radically different way to get what they need: one that’s faster, more intuitive, larger in scope, and less reliant on manual human inputs. This shift will fundamentally disrupt the mobile app landscape, pushing companies to rethink how they deliver value and insights in real time.
SVP of Marketing and Developer Relations at StarTree.
What Is Agentic AI?
Agentic AI refers to intelligent, autonomous agents capable of making decisions and taking actions to achieve specific outcomes for users. Unlike traditional apps, which are narrow in their scope, require users to manually navigate menus, input information, and make decisions along the way, agents take a goal-oriented approach across a broader set of systems. Users simply express their intent—such as booking a vacation under certain constraints or ordering food for the family that will arrive in time before scheduled evening plans—agents interact with multiple data sources and services to fulfill that request seamlessly.
These agents are more than just chatbots or layering a voice interface veneer on top of an existing app. They operate across a wide array of domains and applications. Your company may just be one input in solving the agent’s goal.
They can handle complex, multi-step tasks, anticipate user needs, and personalize interactions in real time. In essence, agents are the next generation of digital experiences—replacing fragmented and rigid apps with fluid, goal-driven interactions.
Why Agents will replace apps
Traditional apps require users to switch between multiple interfaces to complete various tasks. For instance, planning a ski vacation today involves juggling separate apps for flights, hotels, rideshares, lift tickets, ski rentals, snow reports, and dinner reservations. Each step requires manual comparison, schedule coordination, and budget checks, resulting in a fragmented, time-consuming experience with constant back-and-forth decision-making.
An AI agent streamlines this example of trip planning by understanding your budget, schedule, and preferences, then autonomously booking flights, hotels, transportation, activities, and meals—eliminating manual searches and coordinating everything in seconds with minimal input. The agent handles everything by interacting with the necessary services and databases.
This shift will fundamentally change user expectations. People will no longer tolerate clunky apps that demand their time and attention to navigate. Instead, they’ll expect agents to understand their intent and act autonomously, delivering real-time results. Companies that fail to adapt risk becoming invisible in an agent-driven world.
Agents will need access: Agentic AI frameworks as the new APIs
To thrive in this new landscape, companies must make their services accessible to agents. In many ways, Agentic AI frameworks will become the new APIs—the touchpoints through which agents interact with various data sources and operational systems. Just as businesses once rushed to expose their functionality through APIs to be part of the app economy, they will now need to expose themselves to agents to remain relevant.
Emerging frameworks like LangChain and LLM-powered NL2SQL are key enablers for Agentic AI, allowing agents to interact seamlessly with data systems. NL2SQL acts as a lingua franca between human intent and databases, translating natural language queries into precise, actionable insights. By bridging the gap between human-friendly inputs and complex backend systems, these frameworks empower agents to deliver real-time, personalized experiences at scale, without requiring users to navigate traditional interfaces or workflows. Just as APIs allowed legacy systems to become accessible to modern apps, these emerging Agentic AI frameworks can be layered over existing systems to enable agent-driven interactions.
Imagine an AI agent handling this request:
“Order lunch for the team during our scheduled lunch break—something unique, half vegan, half non-vegan, and within our company’s expense policy.”
To fulfill this request seamlessly, the agent must analyze multiple data sources:
- Calendar Data: Checking the scheduled lunch break, attendee count, and location to ensure everyone is accounted for.
- Company Expense Policy: Staying within the per-person budget and ensuring compliance with company guidelines.
- Restaurant Operations: Identifying places with short prep times and available delivery drivers to meet the schedule.
- Order Variety & Preferences: Ensuring half the meals are vegan and half non-vegan, while avoiding recently ordered options for variety.
- Delivery Logistics: Factoring in real-time traffic and route conditions to ensure food arrives on time.
Instead of manually coordinating menus, budgets, and logistics, the agent dynamically orchestrates multiple data sources, ensuring the right food, at the right time, within budget, for the right number of people.
The need for real-time insights
This example illustrates how Agentic AI requires a new breed of analytics to operate effectively. Not all data is the same—some, like company expense policies and calendar events, are static lookups. Others, like restaurant operations and delivery logistics, require real-time analytics to reflect constantly changing conditions.
When an agent needs on-demand insights—such as estimating delivery time—it must query multiple live data streams simultaneously. Traditional analytic databases weren’t designed for subsecond, high-concurrency, multi-dimensional queries at the scale that swarms of agents will require. Data warehouses and data lakes are too slow and static for the fast, automated decision-making agents need.
To generate an optimal delivery estimate, the agent requires continuous, real-time access to restaurant order systems, driver networks, and traffic. The agent must adapt instantly to shifting conditions to provide the best option available at that moment. Without real-time analytic databases capable of handling thousands of concurrent queries, agents will fail to deliver the necessary insights on time. This demands a fundamental shift in data infrastructure—one that prioritizes speed, concurrency, and dynamic query handling.
Companies already leveraging real-time analytics—like Uber, Stripe, and DoorDash—are laying the groundwork with databases like Apache Pinot, which can power instant, high-scale decision-making. The next wave of innovation will build on this foundation, ensuring agents have the real-time insights they need to deliver truly autonomous, intelligent experiences.
What businesses must do to prepare
For businesses to succeed in the agent-driven future, they must focus on several key areas:
- Make data accessible to Agents: Companies need to expose their services and data through agentic AI frameworks, much like they did with APIs during the app revolution.
- Embrace real-time analytics: Businesses must invest in real-time analytic databases that can support the speed, scale, and concurrency required by agents.
- Learn from early leaders: Companies like DoorDash, Uber, and Stripe have already paved the way by embedding real-time analytics into customer-facing apps and are building out their AI strategies.
The future Is Agent-driven
The rise of Agentic AI will disrupt the app landscape as we know it. Users will move away from navigating narrow, static apps toward engaging with dynamic, goal-driven agents that handle tasks autonomously. For businesses, this shift requires a new approach to delivering real-time insights to agents at scale. Those that adapt—by making their data accessible to agents and investing in real-time analytic systems—will thrive in this new era. Those that don’t risk becoming invisible in a world where agents are the new apps.
We’ve featured the best AI website builder.
This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro
https://cdn.mos.cms.futurecdn.net/Wcc69A4Ts8bhSbGgJeGkoZ-1200-80.jpg
Source link
