Why AI Agents Change the Game

In the last decade, our interaction with software products & services has remained limited, constrained by rigid APIs and GUIs.?

In the next decade, our interaction with software products & services will be reimagined, unlocked by the adaptability, reasoning, and learning that AI agents are capable of.?

In short, this shift isn’t just incremental. It’s transformative. Agents reduce friction, unlock scalability, and empower businesses to operate in real-time, with decisions and actions occurring faster than any manual input could facilitate.

AI agents can do what APIs and GUIs can’t: understand context, learn, adapt, and act autonomously. They handle complex tasks, make real-time decisions, and anticipate needs—all without user input, and were not possible until now. ?

For founders and investors, the message is clear: the next wave of dominant tech platforms will be agent-powered. Those who adapt will harness new levels of efficiency, foresight, and market adaptability. Those who don’t risk being left behind operate in a world defined by static APIs and legacy GUIs that defined the previous era.

If you are a founder building the enabling Agent Infrastructure & Applications from the inception idea stage, we’d love to meet you at Underscore VC.

Before we move forward, let’s look back to the previous eras of the web. Why did we require specific interfaces and structured inputs?

GUIs: The Backbone of the Internet Era: User-Friendly but Rigid

GUIs democratized access to technology. However, they also introduced bottlenecks. Users must actively engage with a predefined interface, follow specific workflows, and often encounter limitations based on the developer's foresight. GUI-based systems are inherently reactive—waiting for user input, unable to proactively address problems or anticipate needs. Some call this “client-server architecture,” which is a complicated way to say that the software always awaits input.?

APIs: The Backbone of the Cloud Era: Powerful but Rigid

APIs revolutionized interoperability, enabling systems to talk to one another without manual intervention. They connected systems and let developers build in ways that were previously unimaginable. But they have limitations. They need constant updates, maintenance, and specific commands to get anything done. And let’s face it, they aren’t exactly flexible. Some call this the “micro-services and cloud architecture,” which is a complicated way to say that the software also always awaits input GET requests, or was scheduled for output POSTs.?

Agents: The Backbone of the Intelligent Era: Intelligent and Adaptive

Here’s where agents stand apart. Instead of simply exposing data (APIs) or presenting workflows (GUIs), agents understand goals and act autonomously. They interact with systems dynamically, learning and adapting in real-time. They can do more than just wait. Their core advantage lies in their ability to take action—not just respond to requests. Additionally, Agents can predict outcomes, handle uncertainty, and execute complex tasks across different systems without human intervention.

Let’s break it down: what can only agents do:

Contextual Understanding and Decision-Making

• AI agents can understand the broader context of user inputs and make decisions autonomously. For example, if a user asks for information about "restaurants nearby," an AI agent can factor in preferences (like cuisine type, budget, or diet) from past interactions or even current events (like traffic conditions).
• APIs typically return predefined responses based on specific queries, requiring a human or system to interpret the results and make decisions.
• GUIs offer fixed interfaces and limited decision-making, requiring the user to navigate multiple steps to achieve outcomes.

Autonomous Learning and Adaptation

• AI agents can learn from interactions over time, improving their ability to serve users. For instance, an agent that helps with customer service can learn how to handle complex queries better by analyzing past conversations.
• APIs are static—they don't evolve unless explicitly updated by developers. They rely on a set of defined endpoints with specific functions.
• GUIs do not have the ability to learn or adapt; they are fixed representations that the user must manually navigate.

Multimodal Interaction Across Domains

• AI agents can seamlessly integrate information and functionality from multiple domains without requiring explicit navigation or API calls. For instance, an AI agent could simultaneously manage personal finances, schedule meetings, and handle travel bookings across platforms without requiring the user to log into different apps or interfaces.
• APIs operate in silos; they typically require human orchestration to connect different systems.
• GUIs require the user to manually interact with each application for different functions.

Proactive Task Execution

• AI agents can anticipate user needs and execute tasks without being explicitly told. For example, an AI agent could notice that you're running low on a product and automatically order a replacement.
• APIs and GUIs are reactive; they only respond to user-initiated actions.

Complex, Real-Time Decision-Making

• AI agents can make complex decisions in real-time, often under uncertainty, and execute actions autonomously. For instance, in autonomous driving, an AI agent can decide how to navigate changing traffic conditions without user input.
• APIs require specific instructions and are typically used to execute well-defined tasks.
• GUIs offer a way for humans to interact, but the complexity of decision-making must still be handled by the user.

Human-Like Interaction (That Also Works Machine-to-Machine)

• AI agents can mimic human conversation, emotional intelligence, and social cues, creating more natural user experiences. This includes handling vague commands, asking clarifying questions, and engaging in continuous dialogue.
• APIs offer structured, technical responses that assume a predefined set of inputs and outputs.
• GUIs are limited to pre-designed buttons, forms, and visual elements that require explicit user engagement.

Handling Uncertainty and Dynamic Environments

• AI agents can navigate environments that are constantly changing and where the solution isn’t predefined. For example, an AI agent managing a power grid could dynamically adapt to shifts in demand or unexpected outages without needing human intervention.
• APIs are built for static, predefined scenarios and cannot adapt to new environments unless they are explicitly reprogrammed.
• GUIs are passive interfaces that rely on user input for any change.

In summary, AI agents bring autonomy, learning, adaptability, and proactivity to interactions, which allows them to handle complex, dynamic, and unstructured problems that APIs and GUIs, with their static and reactive nature, cannot tackle on their own.

A Call to Action

For founders thinking about building for tomorrow, we believe now is the time to integrate this vision into your product strategies or create a company around the enabling infrastructure. There are so many opportunities to make a dent here, whether it's in bringing memory, tools, collaboration, task planning & sequencing -- we'd love to hear about the problems and opportunities you see in bringing an agentic future into reality.

Let’s meet for coffee or please request to join our upcoming dinner on this topic if you want to talk more about this:?

Apply for Agents & Infrastructure Dinner in Boston (10/29)