AI Agents: Why Software Is Starting to Look Disposable
-
By
Arthur Kellan
- Technology
- 5 min read
- Technology
- 5 min read
For decades, we’ve all perceived software as a user interface. This means that to perform a specific action, whether it is buying a ticket, calculating a budget, or setting up an advertising campaign, we had to open a specific app or website, understand how it works, click the appropriate buttons, and, finally, wait for the result. This obviously took time and often required significant cognitive effort (especially when it comes to specialized software).
Today, AI agent systems have eliminated this problem by taking over all these tasks. So, all we have to do is set the task and wait for the end result.
What AI Agents Actually Do
First of all, it’s important to understand that autonomous agents aren’t the typical chatbots we’re used to. Instead of simply generating text, they are subjective and can perform a number of other actions beyond communicating with the end user. Specifically, this includes:
Task execution. This means that in addition to providing basic advice (like which hotel to choose), AI agents can also check websites, analyze booking and cancellation processes, enter your credit card details, and complete the booking. A specific example of such an agent is OpenAI’s Operator, which can move the cursor in a virtual browser instead of the users themselves.
Chaining actions. Agents are also capable of breaking down a complex task like “Organize a business trip to London for me” into dozens of smaller, sequential subtasks, requesting human confirmation at each stage whether to move forward or whether the algorithm requires adjustment.
Replacing UI navigation. This is perhaps the most radical feature – the AI agent doesn’t require an intuitive interface, colorful buttons, drop-down menus, or anything else. Instead, it uses only the API or, as an alternative, the page’s HTML code.
From Tools to Systems That Act
Traditional software is passive by default – you still have to interact with it to perform a specific task. The agent-based approach, on the other hand, transforms software into an active system, making it a highly relevant area. Gartner predicts that by 2026 [1], more than 40% of enterprise applications will include embedded AI agents (while in 2025, there were fewer than 5% of such solutions). This means that businesses are increasingly shifting their focus from accessing software functionality to owning the results that this software delivers through AI agents.
Moreover, this transition to AI decision systems also changes software performance metrics. While previously the fundamental metric was monthly active users or the time spent within the interface, in the world of agents, these figures are approaching zero, and the best software is the one in which a user spends almost no time. Another phenomenon we are facing today is the death of the concept of seat-based licensing, as an algorithm can now occupy just one “seat” but be capable of performing the work of, say, ten employees.
It’s also important to understand that traditional tools forget about you as soon as you close a tab, whereas agent-based systems accumulate the context of your previous interactions and tasks. As a result, AI-agentic software becomes proactive, anticipating your needs and preparing solutions to address them even before you fully formulate the next task. This means the era of next-generation workflow automation has already arrived.
Why Traditional Software Starts to Look Redundant
The average agent can seamlessly transfer data from a CRM like Salesforce to Excel, and then to Slack, for example, making interactions between these separate applications seamless (meaning you no longer need to switch between tabs). Therefore, for the end user, the vendor of these tools is no longer important, as the AI agent handles onboarding and navigation itself.
As a result, we face software abstraction: absolutely any software tool becomes a set of capabilities that are called upon as needed, and, if at some point, a more efficient option appears, the agent simply switches to it.
Limits: Reliability, Control, and Risk
Despite their numerous advantages, using AI agents also imposes some risks, such as:
Hallucinations — for example, agents can make mistakes in transactions worth tens of thousands of dollars;
The black box problem (in the enterprise AI context), when it’s impossible to control an agent’s decision chain in a corporate environment due to the opacity of the protocols used;
Security, when allowing an AI agent to use the keyboard and mouse, opens new attack vectors, allowing a hacker to take control through a third-party website.
Enterprise Impact: Fewer Tools, More Orchestration
With the rise of AI automation, businesses are forced to conduct comprehensive reviews of their IT portfolios. Instead of purchasing dozens of highly specialized subscriptions, they are now looking for orchestration platforms to build an agent-based architecture.
Let’s look at the case of OpenAI and Microsoft, for example [2]: integrating agents into the Office 365 ecosystem transformed Word and Excel from editors into performers. Another excellent example is Salesforce, which offers the innovative Agentforce solution [3], which enables automation, thereby eliminating the need to constantly switch between spreadsheet screens.
Conclusion
Of course, software as a phenomenon won’t die, but sooner or later, we’ll all completely transition to a No-UI concept, where interaction with the digital world will become as natural as a conversation with a colleague. This means software will become merely a disposable interface, giving way to action.
Sources:
[2] – OpenAI Agents
Arthur Kellan
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