Why we don't build AI coworkers: our vision for safe, testable, and transparent AI infrastructure

Our philosophy for the future of computing. We're just getting started.

In a world where AI is rapidly evolving, there’s a common narrative that AI is the future of work. The vision? A world where AI coworkers stand shoulder-to-shoulder with humans, taking on tasks, making decisions, and helping to shape outcomes. While that might sound appealing at first glance, we’ve come to a different conclusion: We don’t build AI coworkers.

At bem, our goal is to automate every mission-critical business process on Earth. But we don’t believe in opaque AI coworkers that make decisions without transparency. Instead, we’re committed to building AI primitives—safe, testable, deployable, and observable components that fit seamlessly into existing infrastructures. These AI primitives aren’t meant to replace human coworkers, but to function as dependable tools that give companies the confidence to automate complex processes, without losing control.

Here’s why we’ve chosen a different path.

The Myth of the AI Coworker

The concept of the "AI coworker" promises an agent that can learn on the fly, handle fuzzy logic, and work alongside humans to solve problems. The problem? AI agents are fundamentally flawed. Many are black boxes, operating on proprietary models, generating outputs that even their creators can’t fully explain. In real-world, mission-critical business environments, this lack of transparency can lead to errors that aren’t easily detected or resolved.

Imagine this: You’re a logistics company managing thousands of incoming requests daily. You assign these to an AI coworker, expecting it to route, schedule, and follow up automatically. But what happens when something goes wrong? When that AI coworker makes a subtle mistake in routing or fails to account for a critical human element? Often, you won’t find out until it’s too late.

This is exactly why businesses face resistance in fully adopting AI. Trust and transparency are paramount, and AI coworkers fall short in providing either.

Infrastructure, Not Coworkers

We don’t believe in assigning core operational tasks to AI agents that are hard to debug or audit. Instead, at bem, we build infrastructure primitives—modular AI components that can be trusted, tested, and controlled just like any other piece of enterprise infrastructure.

When you work with bem, you’re not delegating to some opaque AI coworker. You’re deploying infrastructure that can be monitored, versioned, tested, and governed at every step. If something goes wrong, the entire process can be reviewed and observed—every decision point and every transformation is visible to you, the user.

You’re not losing control to AI; you’re gaining more control, reducing complexity, and improving reliability.

Transparency and Observability Are Non-Negotiable

AI coworkers are problematic because they’re inherently opaque. When you ask them to handle a process, they might “learn” from experience or data, but you’ll never know exactly how they made their decisions. This might be acceptable in non-critical applications, but for businesses managing sensitive data, compliance workflows, or supply chains, this is a massive risk.

We’ve seen this in real-world industries: Healthcare companies struggling to trust AI-driven diagnoses, logistics teams uncertain whether their orders were routed correctly, or financial services firms hesitant to hand over compliance processes to AI. At bem, we believe businesses shouldn’t have to “hand over” anything to AI; they should deploy AI components that they can observe, audit, and trust.

That’s why we build for transparency first. Every transformation, routing decision, and action is logged, observable, and testable. Our primitives are part of your infrastructure, subject to the same monitoring and governance standards you’d apply to any other mission-critical software.

AI Coworkers Fail in Complex, Mission-Critical Workflows

The allure of AI coworkers is that they promise to handle complex, high-stakes workflows—like onboarding customers from legacy systems or processing hundreds of real-time requests. But here’s the issue: AI coworkers break down when they’re faced with complex, nuanced business logic.

AI coworkers might work in simple, deterministic workflows, but as soon as you introduce real-world fuzziness—like ambiguous customer emails, conflicting schedules, or incomplete data—they struggle. We built bem to combine both deterministic and fuzzy logic into manageable, transparent, and deployable components that give businesses more control, not less.

Our goal is not to replace your decision-makers with AI. It’s to give them tools that simplify the complexity of your operations, that remove the technical debt, and reduce error rates—all while giving humans the final say.

The Future: Automating Safely, Not Automatically

The vision of a workplace populated by AI coworkers suggests that everything should run automatically without human intervention. But we believe the future isn’t about removing humans from the loop. It’s about empowering humans with infrastructure that allows them to scale their operations safely and confidently.

At bem, we build AI primitives that automate the hardest parts of your business processes, but always with observability and manual intervention in mind. You can pause, review, and override the logic at any time. This is why our approach is fundamentally different. Our tools don’t replace your people—they augment their capabilities by handling repetitive, complex data processing tasks and routing logic, while leaving critical decisions in the hands of experts.

Conclusion: AI as Infrastructure, Not Coworkers. Here’s the new generation of stochastic abstractions

At bem, we’re not here to build AI coworkers. We’re here to build safe, testable, and deployable AI infrastructure that scales with your needs without giving up transparency or control. We believe that AI should be treated like any other infrastructure—built on solid foundations, observable at every step, and trusted to perform reliably in mission-critical environments.

The world doesn’t need more AI agents. It needs AI primitives that are robust, flexible, and give businesses the confidence to automate without losing sight of what matters most—safety, reliability, and trust.

We’re building that future. Let’s automate business processes with AI that works with you, not instead of you.