Designing an Agent Kernel for Neuro-Symbolic Systems

Modern agent stacks are converging on a familiar truth: without kernel-grade primitives, reliability is accidental. I am working on Splendor AI to formalize those primitives, borrowing from operating systems, distributed systems, and safety engineering.

Why a kernel, not just a framework

Traditional frameworks prioritize features. Kernels prioritize invariants. The goal is to make state loops, reward functions, and symbolic constraints first-class citizens so that agent behavior is auditable and composable.

Design goal

The kernel enforces safety checks before any agent commits state. This creates a predictable boundary for evaluation and continuous testing.

Building the execution loop

Each agent run must expose a transparent control loop. The kernel standardizes hooks for observation, planning, execution, and verification.

type AgentStep = {
  state: Record<string, unknown>
  action: string
  reward?: number
  guardrails: string[]
}

This contract makes it possible to build system-wide tooling around replay, auditing, and recovery.

Next focus areas

Multi-tenant scheduling for distributed agents
Policy evaluation layers for safety audits
Rust core with Python interfaces for research velocity

If you want to collaborate on kernel-grade AI primitives, reach out directly.