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.
The Two Pillars of Scalability
To understand this convergence, we have to look at the two distinct approaches that are currently merging.
1. The System View: The Agent OS
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.
Key components of this approach include:
- POSIX-Inspired Primitives: Just as an OS uses strictly defined system calls, an Agent OS must use defined primitives for actions.
- The 90/10 Rule: To minimize latency and cost, 90% of an agent’s operation should be lookup (retrieval, cache, defined tools) and only 10% should be generative reasoning.
- Semantic Firewalls: Safety isn’t a “please don’t do that” prompt; it is a rigid policy engine (like OPA/Rego) that ensures 0% violations.
2. The Model View: Mixture of Experts (MoE)
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.
- Sparse Activation : The model routes queries to specialized “experts.” This reduces compute cost and increases efficiency, conceptually identical to “Scale by Subtraction.”
- Tool Primitives : The model views external tools (browsing, code execution) not as abstract concepts, but as extensible primitives it can invoke dynamically.
The Convergence: Where the OS Meets the Model
When we mapped my system patterns against Grok’s internal design, four distinct alignments emerged. These patterns suggest a blueprint for the future of AI engineering.
I. Modularity is the New Monolith
In agent-os, I avoid monolithic codebases in favor of decoupled components. We use registries (like the Agent Tool Registry) to avoid hardcoding capabilities.
Grok mirrors this at the inference layer. By using MoE, the model avoids a “monolithic brain” approach. It routes tasks to specific experts.
The Insight: Whether you are building the container or the intelligence inside it, composability is key. We are moving away from “one giant prompt” and “one giant model” toward orchestrated swarms of specialized functions.
II. Governance: Asking vs. Enforcing
One of the biggest risks in Agentic AI is relying on the LLM to police itself via system prompts. My architecture introduces agent-mesh, a nervous system that handles identity, trust, and policy outside the model’s context.
Grok reveals that at the model layer, xAI enforces similar “immutable safety instructions.” These are not probabilistic suggestions; they are hard guardrails.
The Insight: Safety must be deterministic. In my OS, a semantic firewall blocks the action. In the model, safety instructions supersede generation. Both agree that probability is not a strategy for security.
III. Context Engineering: The Fight Against Rot
In my “Scale by Subtraction” series, I discuss “context rot”, the degradation of performance as prompt contexts grow. I advocate for Temporal Indexes and Knowledge Graphs to feed agents only what they need (Frugal Architecture).
Grok’s internal logic mirrors this through “sparse attention.” It optimizes context windows and uses external tools to “search” memory rather than holding everything in a brute-force buffer.
The Insight: Memory should be an action, not a storage container. Agents should “Google” their own long-term memory rather than carrying it all in their active RAM.
IV. Scale by Subtraction
This is the heart of my philosophy. To scale a system, you don’t add more features; you remove dependencies, friction, and noise.
Grok’s MoE architecture is the physical manifestation of this philosophy. By activating only a fraction of parameters for a given token, the model “subtracts” the unnecessary noise of the rest of the network.
The Insight: Efficiency isn’t about moving faster; it’s about doing less. Whether it’s an agent looking up a cached answer (90% lookup) or a model routing to a single expert, the goal is to cut the computation path to its absolute minimum.
The Verdict: Toward a Universal Agentic Standard
The conversation with Grok confirmed that we are approaching a standardization of AI architecture.
We are leaving the era of the “Black Box” AI, where we throw inputs in and hope for the best. We are entering the era of Agentic Operating Systems , where the Model is the CPU, and the System is the Kernel.
If xAI and other labs are building the processors of the future, then projects like agent-os are the Linux kernels that will run on top of them. The principles are the same: Modularize. Enforce. Subtract.
Originally published at https://www.linkedin.com.
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