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For decades, software interacted with software through a simple mechanism: APIs.

Application Programming Interfaces became the plumbing of the digital economy.

Payment platforms expose APIs.
Cloud providers expose APIs.
Enterprise software exposes APIs.

If a system needed to interact with another system, the interface was clear and deterministic. Developers wrote code that called those APIs, executed commands, and returned structured results.

But the rise of AI agents may change how software connects to the world.

And that raises a new question:

Who controls the interface between AI and software?

The first wave of AI systems focused primarily on information.

Models answered questions, summarized documents, translated text, and generated content.

But the next wave is increasingly focused on execution.

AI agents are being designed to:

Book travel.
Generate reports.
Update CRM systems.
Run analytics workflows.
Execute multi-step business processes.

In other words, AI is moving from thinking to doing.

And doing requires access to systems.

Until now, the bridge between systems has been APIs.

An API defines exactly how software should interact with another system:

What request to send.
What parameters to include.
What format the response will take.

This approach works extremely well when software is deterministic and predictable.

Developers know exactly what instruction to send.

But AI agents operate differently.

They reason about goals rather than follow rigid scripts.

Which raises an important challenge:

How should an AI system discover and use the tools available to it?

A new idea is beginning to circulate in the AI ecosystem: standardized interfaces for AI agents.

Instead of hard-coding every API call, systems could expose their capabilities through protocols specifically designed for AI models.

One example is the Model Context Protocol (MCP), an emerging proposal for how AI systems could discover and use external tools.

The idea behind MCP is simple.

Systems describe the tools they offer in a standardized way, allowing AI agents to discover, understand, and use those tools dynamically.

Rather than writing explicit integrations for every service, agents could interact with a structured layer that explains what actions are possible.

In theory, this could make the ecosystem far more flexible.

But it has also triggered debate.

Some developers argue that traditional APIs are sufficient.

Others believe a new abstraction layer will be necessary as AI systems become more autonomous.

At first glance, this may sound like an internal developer discussion.

But history suggests something deeper is at stake.

Control over interfaces has always been one of the most powerful positions in technology.

Operating systems controlled the interface between users and computers.

Browsers controlled the interface between users and the web.

App stores controlled the interface between developers and consumers.

Cloud platforms control the interface between companies and computing infrastructure.

Each layer created new gatekeepers — and enormous value pools.

The question now is whether AI agents will create another interface layer.

As AI agents become more capable, the ecosystem could evolve in several directions.

In this scenario, agents simply learn to call traditional APIs.

Developers continue to expose functionality the same way they always have, while AI systems become better at orchestrating those tools.

This would represent an evolutionary change rather than a structural shift.

Alternatively, standardized protocols like MCP could become the preferred way for systems to expose capabilities to AI.

This would create a new abstraction layer between AI agents and traditional software.

If widely adopted, such standards could reshape how integrations are built across the industry.

A third possibility is that large AI platforms themselves become the primary gateway to software systems.

Instead of agents connecting directly to thousands of tools, they would rely on ecosystems controlled by a few major platforms.

This model would resemble the evolution of mobile ecosystems, where app stores became the dominant distribution channel.

Regardless of which path emerges, one thing is clear.

AI is forcing the industry to rethink how systems interact with each other.

When software becomes capable of reasoning and executing tasks autonomously, the traditional boundaries between applications begin to blur.

Instead of users navigating software interfaces, AI systems may increasingly orchestrate multiple tools behind the scenes.

The visible interface becomes smaller.

The orchestration layer becomes more important.

For companies, the implications extend beyond developer architecture.

The ability to expose systems in ways that AI agents can easily understand and use may become a competitive advantage.

Organizations will need to think about:

How their services are accessible to AI systems.

How workflows can be automated through agent-driven processes.

How their data and capabilities are exposed to external ecosystems.

In a world of AI agents, software is no longer just built for humans.

It is increasingly built for other software systems to operate autonomously.

The rise of AI agents is forcing a fundamental question.

How should machines interact with the digital world?

Whether the answer remains traditional APIs or evolves into new protocols, one thing is becoming clear.

The next battle in software may not be about models.

It may be about interfaces.

Because whoever controls the interface between AI and the digital world may control the next generation of platforms.

Until next time,
Stay adaptive. Stay strategic.
And keep exploring the frontier of AI.

Fabio Lopes
XcessAI

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