Why the concept of AI Agents remains inherently limited

The challenge is to rethink AI entirely!

Google’s Gemini 2.0 is undoubtedly a significant step forward in AI development. The system introduces advanced multimodal capabilities, enabling it to process and generate text, images, and audio seamlessly. More importantly, it introduced experimental “agentic” functionalities designed to perform complex tasks with minimal human intervention. These agents represent a step toward more autonomous AI, capable of integrating into workflows and simplifying tasks for users.

However, despite these advancements, the concept of AI agents remains inherently limited. While agents aim to anticipate user needs and perform tasks independently, they still fall short of the vision they aspire to fulfill. The issue isn’t just about technical challenges — it’s the underlying concept of an “agent” itself.

Agents: A Step Forward

AI agents attempt to replicate the role of a personal assistant. They are designed to handle tasks like responding to commands, automating workflows, or even navigating the web to shop or gather information. Google’s Gemini 2.0 powers agents like Project Mariner, which can autonomously browse and perform multi-step actions online, and Project Astra, which integrates with Google’s services to offer real-time, contextual assistance.

These tools showcase the potential of AI to perform more complex and layered tasks. They represent progress in making AI systems more useful and capable of operating across multiple domains. For example, an AI agent could coordinate your schedule by booking appointments, sending emails, and managing conflicts — all without constant micromanagement.

The Inherent Problem with Agents

The core issue with AI agents lies in their design as distinct, interaction-driven entities. These systems are fundamentally built to respond to user input — waiting for prompts, performing specific tasks, and delivering outputs. While they represent progress in automating multi-step processes, this interaction-based framework limits their potential to provide meaningful, proactive assistance.

Agents lack the ability to act seamlessly within the context of your environment. They operate as tools that require engagement rather than as systems that naturally support and enhance workflows. From a user’s perspective, this design creates unnecessary friction. Instead of reducing cognitive effort, agents introduce the need to manage, command, and ensure they’ve executed tasks properly.

In contrast, the real future of AI lies in its ability to integrate into our lives without the need for constant interaction. Systems that are deeply embedded in their context — enabling them to observe, adapt, and act without explicit direction — offer far greater value. This is where the concept of AI as a layer comes into play.

A Better Future: AI as a Layer

Instead of thinking of AI as agents we interact with, we should envision AI as a seamless, integrated layer that enhances human perception, decision-making and actions. This isn’t about AI stepping in to “help” us when asked; it’s about AI quietly working in the background to enrich our interactions, environments, and actions without needing to announce itself.

Imagine smart glasses that subtly guide your attention by dimming distractions or brighten up key details. Or consider earbuds that, as you meet someone, discreetly remind you of your last conversation with them — enhancing your focus and effectiveness without requiring you to explicitly engage with the system. Even a computer that offers key information during a meeting without a single prompt — responding to your needs before you articulate them.

This is what AI should aim to be: not an agent that performs tasks when called upon, but a constant, unobtrusive presence that improves the way we navigate the world. It’s not about replacing human effort or decision-making but about making us better at both.

Layers outperform agents because they don’t behave as distinct entities. They don’t require interaction or instruction. Instead, they integrate directly into our sensory and cognitive processes, amplifying our abilities without creating new dependencies. They act as enhancers rather than assistants.

By conceptualizing AI as a layer rather than an agent, we can move beyond the constraints of “reactive” systems and toward truly symbiotic AI. It’s not about asking an AI what to do; it’s about the AI making our choices clearer, our environments more accessible, and our lives more efficient without interrupting us.

The Way Forward

AI agents, as they exist today, are an important milestone. They demonstrate how far we’ve come in automating complex tasks and creating systems capable of independent action. But the concept of an agent is ultimately a stepping stone, not the destination.

The future of AI isn’t a chatty assistant or an agent waiting for instructions. It’s a quiet, integrated presence — a layer of intelligence that surrounds and supports us, amplifying our capabilities without demanding our attention. That’s where AI can achieve its true potential: not as something we interact with, but as something that transforms how we perceive, think, and act.

This is the shift we need in AI product development. The challenge isn’t to build better agents; it’s to rethink AI entirely. The future isn’t about designing a perfect assistant — it’s about designing a perfect layer.