Let's Retire the Human Middleware and Move to Systems of Intelligence: It's Better for Everyone
The industrial revolution of context has begun, and its first victims are the armies of human routers once known as middle management. In the United States alone, roughly 13 million people carry a management title, about eight percent of the workforce, with a median wage just over $122,000 a year according to bls.gov . Pare back even a quarter of those roles and the payroll freed tops four hundred billion dollars annually. That's money that can be redeployed rather than evaporating in what Bain & Company famously calls "organizational drag," the friction that saps as much as forty percent of a firm's productive power. The bar chart below sets that saving beside the far larger prize sized by McKinsey: generative AI-driven systems of intelligence could unlock between $2.6 trillion and $4.4 trillion in fresh economic output every year. Eliminating human middleware doesn't merely cut costs; it clears the runway for decisions that arrive at machine speed.
Faster and Cheaper, the promise of Systems of Intelligence
For decades, organizations have relied on armies of "human middleware" whose primary job has become routing vague business intent through convoluted processes and disjointed systems. Being a human router of information is not a great job. You're constantly under pressure from the principals who own decisions, budgets, and execution, as well as the people who do the work. Neither side really appreciates the human routers, but they also don't know what to do without them.
2016 Greylock essay coined "systems of intelligence" as the third great software layer, built atop systems of record and engagement, to convert the exhaust of structured and unstructured data into predictions and automated decisions. This creates a moat deeper than mere user interface or data ownership could offer.
2025 manifesto updates the idea for the age of foundation models , arguing that modern systems of intelligence ingest raw multimodal data, fuse it into a living context graph, and then act autonomously across silos. This collapses not only legacy SaaS but the bloated services economy surrounding it.
The emergence of SOIs marks a significant shift away from manual intent routing towards automated context engineering: systems that intelligently understand and execute business intent autonomously. But achieving this transition is anything but trivial. It represents a deep technical and philosophical transformation in how enterprise systems are designed and managed.
The Rise of Context Engineering
The core challenge in replacing human middleware isn't simply automating workflows. It's effectively managing and engineering organizational context at scale. Context engineering involves capturing, maintaining, and leveraging precise, real-time information about organizational state, enabling intelligent, autonomous decision-making.
Unlike traditional automation, which rigidly follows predefined procedural rules, context engineering requires systems capable of dynamically interpreting complex organizational environments. This balancing act demands careful management of context depth, providing just enough information to enable accurate, informed actions without overwhelming the system or users. This is a tricky exercise. On the personal level, it's more an art than science, as the rise of "vibecoding" has shown . On enterprise scale, it's a whole different beast, and that's what we do every day at Beyond Work.
Building Systems of Intent and Intelligence
At Beyond Work, we've learned through extensive experimentation that two core components are essential to successful context engineering: a system that can both observe itself and reconcile. We call these systems of work.
Observers are intelligent monitoring agents continuously assessing the organization's current state against its predefined intentions. They perform real-time analytical queries on dynamic context graphs, identifying discrepancies or misalignments instantaneously.
Reconcilers , on the other hand, are proactive autonomous agents triggered by Observers when discrepancies arise. They autonomously execute tasks to realign the organization's state with its intended outcomes. This approach dramatically reduces manual intervention and latency in resolving issues.
Workblocks are the core functional units tying it all together. Unlike traditional enterprise apps, they are modular agentic blocks that bundle both the automation and the app for human interaction. They're created with natural language but remain static when they operate.
As a whole, the system might look and feel like your legacy SaaS app, but it's dynamic, can be updated on the fly, and expanding it is as easy as dropping in a new workblock and setting its intent.
Implementing context engineering at scale introduced significant technical challenges, particularly around managing complexity and ensuring real-time responsiveness. At Beyond Work, the solution was a robust, dynamically updated context graph: a sophisticated interconnected structure maintaining live states of employees, suppliers, invoices, policies, and more. This graph allows us to not only manage complex systems of work but also store real-time learning from end users every time they have feedback on the content or context. This is how we calibrate our systems of work to interact with users and manage the tricky third part of context engineering: human intent.
This isn't theory. We are already in production at large scale, processing millions of workblocks across logistics, life sciences, and energy with Fortune 500 brands.
The Future Workforce: From Middleware to Strategic Context Architects
This technological shift profoundly transforms workforce roles. Employees transition from "human middleware" to strategic "context architects," responsible for defining, refining, and overseeing organizational intent. Managers evolve into strategic curators who proactively shape organizational direction rather than reactively manage tasks. It's hard to imagine future enterprise jobs that don't come with doing work, not just routing it, and I think that's inherently a great thing. No one should just be a middleware component.
Conclusion: The Technical and Organizational Revolution
Moving from human middleware to advanced context engineering isn't merely a technology upgrade. It represents a fundamental transformation in organizational philosophy and operations. Through intelligent automation, dynamic context management, and agentic AI, businesses can achieve unprecedented levels of responsiveness, productivity, and strategic alignment.
At Beyond Work, the technical journey has illuminated both the complexity and potential of engineering such systems. The lessons learned and technologies developed underscore a future where intelligent systems not only replace tedious manual coordination but also empower human creativity and strategic thinking.
In this new era, context is no longer merely data: it's intelligence itself, driving organizational effectiveness to levels previously unimaginable.
First published on LinkedIn, 29 June 2025.