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ArcKernel

ArcKernel offers a runtime governance infrastructure that embeds deterministic policy enforcement directly into the inference loop of AI agents, ensuring every API call, decision, and outcome complies with predefined constraints. The platform provides modular governance modules, identity persistence across sessions, and a full audit trail to meet regulatory requirements such as the EU AI Act, while remaining compatible with any model architecture.

Wilmington, United StatesFounded 2026310+ followers
Updated 2 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Current AI agents operate as black boxes, making it difficult for organizations to enforce deterministic policies, track decision pathways, and demonstrate compliance with emerging AI regulations. Existing guardrails are often applied post-hoc, lacking real-time control and auditability, which leads to unpredictable behavior and regulatory risk.

Solution

ArcKernel provides a runtime governance infrastructure that embeds deterministic policy enforcement directly into the inference loop of AI agents. By integrating modular governance components, the platform ensures that every API call, decision, and outcome adheres to predefined constraints, regardless of model size or session context. Identity persistence maintains agent roles, constraints, and intent across sessions without relying on retraining or large context windows. The system also generates a complete audit trail for each decision path, enabling organizations to prove intent and comply with regulations such as the EU AI Act. This approach transforms AI governance from reactive guardrails to proactive, real-time alignment.

Target Audience

ArcKernel targets enterprises, regulated industries, and AI platform providers that require real-time control, compliance auditing, and reliable alignment of autonomous agents.

Features

  • Modular runtime governance engine that enforces deterministic policies during inference
  • Identity persistence with O(1) symbolic recall, preserving agent roles and constraints across sessions
  • Comprehensive audit infrastructure that traces, validates, and attributes every decision path
  • Compatibility with any AI model or context length, providing consistent alignment regardless of underlying architecture
  • Built-in adversarial resilience to protect agents from malicious inputs at runtime
  • Scalable API that supports governed calls across multiple modules and services
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