The explosion of generative AI and the budding experiments with agentic applications created a slew of products touting the ability to run our digital lives autonomously. Yet these products suffer from a common flaw: they are designed to operate user interfaces that are themselves designed to be operated by human beings.
The makers of these products congratulate themselves on the ability of their offerings to use web browsers, fill out UI forms, navigate web pages and follow links.
This is waste: an AI agent without eyes doesn't need a UI and given the increasing cost in dollars and kilowatts required to run generative AI applications, we’re bound to see more waste as these products and services gain mass adoption.
Taking the Scenic Route
The result? AI agents will be slower given the need for a UI layer. Further, AI agents will run into the same obstacles human users do when using a typical web application:
- counterintuitive UI design
- confusing UI flows
- lack of accessibility
- broken web apps
Agents will be slower given the need to contact a command-and-control process to issue instructions to a peer process driving a web browser, which then completes—or fails to complete—a user request, which then sends results back to the command-and-control process, which then digests the result and finally informs a user.
This is unjustifiably wasteful irrespective of implications for increased emissions and overconsumption of electricity–to say nothing of the financial cost of a request to an AI application relative to a RESTful API.
Design Dilemma
Why build this way? Why the curious obsession with technology that accomplishes human goals with superior results but always, as the song goes: like humans do?
Solutions in AI or robotics that garner the most praise or elicit the most fear are those that mimic human behavior: humanoid robots that work in factories or AI that operates your desktop computer.
These innovations evoke such fear because of a flaw in design philosophy apparent to anyone with eyes.
Robot Dreams
These innovations are designed to replace human beings.
This leads to a troubling design approach. When the frame of reference for any AI solution is that of human beings and how they might accomplish a task, the result is androids and other Asimovian robot dreams.
Never mind that humans and their bodies make poor machines, both in practical and philosophical terms. If the design prompt, or the assumption within it, is to make a human but better, the solution will prioritize human mimicry over every other criterion.
I spent the summers of my college years working at a Toyota plant in my home state of Kentucky. It was a dazzling operation combining both human and machine innovation. Industrial robots abounded yet none of them looked like a human being.
And why should they?
Automated stamping of car bodies in the most efficient way requires unique capacities and even a super humanoid robot makes less sense than the industrial robots in use today.
Human Machines | Machine Humans
Human bodies evolved in response to unique environmental requirements irrelevant in modern industrial contexts. In such contexts, what is required is a solution sensitive to the operational challenges in question, not a superhuman substitute.
The similarities to agentic application design are striking. We see the same approach centering human mimicry: an agent autonomously clicks and navigates through your web browser or desktop UI, doing your tasks largely in the way you yourself would do them.
Humanoid robots and AI-enabled self-driving user interfaces may inspire fear or awe but if the aim is achieving the objective in the most effective efficient way possible, attempts like these will routinely fail.
The extraordinary cost to maintain AI applications demands design approaches that leverage the strength of the underlying technology and avoid the urge to mimic human UI interactions.
Alternative Approaches
What is an alternative?
It involves using LLMs (large language models) to do what they excel at: extracting user intent from natural language. Traditional APIs, on the other hand, excel at taking structured data as a request and executing business logic in response. By combining these strengths, we can build efficient and effective AI applications that avoid unnecessary complexity and waste.
In the next post, we'll explore a real-world example app where, instead of using massive sets of training data, I use basic prompt engineering and a simple REST API to create a natural language Uber app.
Stay tuned.
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