Tools: Agentic Mirror: When System Architecture Meets Model Design 2026
Posted on Feb 4
• Originally published at Medium on Feb 4
How a conversation with Grok revealed that the principles of “Scale by Subtraction” apply equally to the Operating System and the Model.
For the past year, my work has been obsessed with a singular problem: How do we graduate AI from chatbots to robust, deterministic systems?
Across my open-source work on agent-os and agent-mesh, and my writing on “Scale by Subtraction,” I’ve argued that we cannot prompt-engineer our way to safety. We need architecture. We need kernels, control planes, and semantic firewalls. We need to treat AI agents not as magic boxes, but as software that requires a nervous system. Scale by Subtraction
Recently, I sat down with Grok (xAI’s LLM) to compare notes. I laid out my architectural patterns, POSIX-inspired kernels, OPA policy enforcement, and 90% lookup/10% reasoning workflows. In return, Grok shared insights into its own high-level architecture, Mixture of Experts (MoE), sparse activation, and modular tool primitives.
The result was a striking realization: We are building the same machine from opposite ends.
While I am architecting the Operating System (the environment, governance, and boundaries), xAI is architecting the Processor (the model). And surprisingly, both rely on the exact same design philosophy: Modularity, Deterministic Safety, and Efficiency via Subtraction.
To understand this convergence, we have to look at the two distinct approaches that are currently merging.
My work, particularly on agent-os and agent-mesh, is built on the belief that agents need a Kernel. In traditional computing, the kernel manages memory, processes, and safety. It doesn’t “hallucinate” resource allocation; it enforces it.
On the other side, Grok (and models like it) utilize a Mixture of Experts architecture. Instead of a dense, monolithic model where every parameter fires for every query, MoE uses a “sparse” approach.
Source: Dev.to
