Software for AI Agents Will Be Completely Different Than Software for Humans

The Beginning of the AI Agent Era

Photo by Tobias Kebernik on Unsplash

Over the last few months, it’s become clear to me that software built for AI agents is going to look entirely different from software built for humans. While that might sound obvious at first, I’ve come to this conclusion through hands-on experience building an AI agent called OllyChat, which handles site reliability engineering tasks. The architecture I use in OllyChat is similar to many AI agent applications, and it reveals some fundamental shifts in how these systems will be designed and used.

The Emerging AI Agent Architecture

Below is a simplified diagram of the AI agent architecture I’ve been working with and how it fits into a modern organization:

Here’s the high-level flow:

1. A human “manages” the AI agent through a natural interface—text or voice (via Slack, Teams, phone calls, etc.).

2. The AI agent creates plans, retrieves data, executes tools, evaluates progress, and iterates until it completes its goal.

3. Foundation models (OpenAI, Anthropic, Deepseek, etc.) provide the underlying language and reasoning capabilities.

4. Data, Tools, and Evaluators provide the AI agent with raw data, task automation, and performance feedback.

This setup is already very different from the software we build for humans, where we design user interfaces— screens, buttons, and dashboards—around human cognition and time constraints. AI agents aren’t held back by those same constraints.

Key Implications of This Architecture

User Interfaces for AI Resemble Human Interfaces

The “UI” of AI software is essentially text or voice, mirroring how humans manage each other today. The typical “AI Agent Manager” role will look a lot like managing a human subordinate—communicating tasks, checking progress, and giving feedback—but done via chat or calls.

Native Access to Company Data

AI agents need access to data at machine speed. Traditional dashboards and summaries exist because humans can’t process massive amounts of raw data in real time. AI agents, however, can. To provide the best responses and decisions, they’ll want direct access to the raw information. This means new types of real-time data pipelines that deliver high-fidelity data on demand.

Native Access to Tools

AI agents will also need ways to “do things” within the organization at machine speed. Rather than forcing an agent to wait for a human to click a button or parse a complicated dashboard, a robust AI-oriented interface will let the agent seamlessly orchestrate these tools. Imagine an interface that looks more like an API for a step-by-step debugging console than a web-based UI

Domain-Specific Customization Still Matters

Even with incredible foundation models, there’s a lot of custom code to write. Each industry, organization, or team will have specialized processes, constraints, and knowledge—requiring a tailored AI agent layer to get high-quality results. General-purpose models are starting points, not turnkey solutions.

AI Agents Improve Over Time

Much like a new hire, your AI agent will learn from every task and interaction, getting “sticky” because its performance compounds. It observes the outcomes of its actions, the feedback from humans, and the subtle signals in the data, steadily refining its approach.

Market Implications

Foundation Models Become Commodities

Switching foundation models will become almost frictionless. AI agents will dynamically pick the best model for a specific task—like your computer might switch between GPUs depending on which is best for the job.

Software Designed for Humans Will Be Repurposed or Superseded

Today’s software stack is heavily oriented around human-driven interfaces: dashboards, forms, notifications, and ways to get other humans to use the software. In an AI-first landscape, these tools will be controlled by agents that read and write data at scale, with humans only occasionally stepping in to provide high-level direction.

AI Replaces (Some) Human Labor Over Time

Unlike current tools that augment human productivity, AI agents replace tasks. This won’t happen all at once, but we’ll see an incremental process where tasks naturally migrate from humans to AI systems over the course of years.

Companies Shift Toward Managerial Labor

In the near future, the majority of a company’s workforce may function in “manager roles,” spending upwards of half their time directing AI agents. Coordinating tasks across AI-driven systems could become a primary job function, while AI agent managers also collaborate with each other.

Human-scale vs. Machine-scale

One of the biggest conceptual leaps here is recognizing that the constraints of human-scale processes—like waiting for daily or weekly reports, or manually debugging issues—don’t apply to AI agents. They operate at machine-scale, where data flows continuously and thousands of tasks can be executed in minutes, not days.

Human-scale

• We open a dashboard once a day, check metrics, then spend hours deciding and executing next steps.

• Information is heavily summarized so we can digest it.

• We rely on specialized tools that we can comfortably manipulate by hand.

Machine-scale

• An AI agent constantly ingests live data streams, adjusting its actions on the fly.

• Data is raw, granular, and massive—no summarization needed until it has to report to a human.

• Tools are accessed through direct APIs and complex orchestrations that can happen thousands of times in a single day.

The AI Agent Era

This shift to AI agent–driven software is poised to be as transformative as the move to personal computing or cloud infrastructure. We’re only at the beginning of seeing how AI agents will fundamentally change workflows, data pipelines, and tool interfaces. But one thing is certain: software for AI agents must be built for machine-scale operations rather than retrofitting the constraints of human-focused designs.