LLMs acting on behalf of humans and interacting with real-world systems isn't theoretical anymore - with the advent of function calling, it is now a reality. And with Arcade, function calling becomes a superpower that connects LLMs to authorized APIs, user services, and complex systems. With this shift, we're seeing the emergence of a new software practice: Machine Experience Engineering (MX Engineering).
The Current Reality
For AI models to handle our emails, schedule appointments, and manage documents, they need agents and tools connected to our digital lives. But here's the key shift: when an LLM calls a tool, it's not just another I/O instrument. The LLM is the consumer. The user. And it comes with its own computational reasoning patterns and behaviors.
This fundamental change means software engineers are now building for a new class of users - massive matrices capable of reasoning. This isn't just an API integration problem - it's a new engineering paradigm.
The Engineering Challenge
As engineers, we have deep intuition about how humans interact with our systems. When we develop a CLI, REST API, or Python package, we intuitively know how other developers will shoot themselves in the foot. We build guardrails for those exact scenarios.
But LLMs don't reason like we do. The kinds of mistakes they make are not intuitive to us. Every interface we have today was designed with human users in mind, yet we're asking these systems to consume them all and act on our behalf.
"This is not going to end well," you might think. And you'd be right.
Why Traditional Interfaces Fall Short
Tools designed for LLMs will rarely map 1:1 to third-party REST API endpoints or any existing interface. Doing so would set LLMs up for failure. Instead, we need to ask: "What jobs will the LLM need to get done in this context?"
The tool's name, its interface, each argument we expose, and how the response is structured - everything needs careful consideration. Inside and around our tools, we have to do whatever software and networking gymnastics necessary to make the LLM successful in its job.
MX Engineering Principles
Just as we have Frontend Developers, Designers, and UX Engineers dedicated to human user experience, we need MX Engineers focused on machine experience. This means putting ourselves in the LLMs' shoes, diving deep into their way of 'thinking', and anticipating how they'll stumble.
Every day at Arcade, we're learning something new that allows us to mature our practices and understand which patterns work. Tools developed with this mindset consistently outperform naive implementations. Anyone serious about building LLM-powered products wants well-designed tools like this.
The Reality of Tool Design
It's easy to build integrations. But designing great tools for LLMs is hard. MX Engineering deals with multi-faceted problems and requires creativity and deep thought. We have to place guardrails so that LLMs have just enough freedom to do what is right for the job, while preventing common failure modes.
Through practical implementation, we're discovering patterns that work and those that don't. This isn't theoretical - these decisions directly impact whether an LLM can reliably perform its tasks.
Looking Forward
MX Engineering is rapidly evolving, with new patterns and practices emerging daily. For engineers fascinated by LLMs, agents, and tools, this is an extraordinary moment in software development. We're not just building integrations - we're creating a new engineering discipline.
The difference between simple integrations and true MX Engineering becomes clearer every day. As we continue to understand how LLMs interact with our tools, we're laying the foundation for the next generation of AI systems. If you're passionate about this emerging field like we are, check out our open roles. We're looking for engineers ready to help define its future.