Building a Runtime Stability Framework for Autonomous AI — from Research to Working Prototype

A⚙️ Abstract

Over the past months we’ve been developing a modular framework for real-time stability monitoring and self-regulation in AI systems.
The concept — internally codenamed ISM-X / RSC Stack — defines how autonomous agents can continuously measure their internal coherence, detect phase drift, and adaptively control their reasoning intensity or decision gating.

This article presents the current public architecture and project stage.
All critical core algorithms remain confidential and protected under trade-secret status.
However, the surrounding system — architecture, runtime, and observability stack — is open for review and potential collaboration.

🧩 The Core Idea (High-Level)

Every complex agent generates signals that describe its own state: semantic coherence, prediction stability, drift, loop gain, etc.
We treat these as vital signs — runtime telemetry for cognition.

Our framework defines:

how to collect and normalize those signals,

how to compute an abstract stability index (Γ) and a phase offset (Δφ),

and how to classify each state into lock, mini-lock, or out-of-lock regimes.

The internal mathematical transformation that governs this process remains proprietary.
What’s published here is the operational shell — a safe, auditable, and high-performance runtime environment.

🧱 System Architecture (Public Layer)

[Agent Loop] → metrics → [RSC Core] → {lock / mini-lock / out-of-lock}
                                  |
                                  v
                          Secure Collector (JSONL)
                                  |
      +---------------------------+---------------------------+
      |                           |                           |
  Prometheus Exporter        Web UI (FastAPI)            Alert Daemon
  (KPIs for Ops)            (Live monitoring)           (Webhook / SLA)

Modules included in the public stack:

runtime_adapter – standardizes input signals.

rsc_collector_v12 – high-speed JSONL collector with rolling checksum and optional AES-GCM encryption.

rsc_prom_exporter – exposes KPIs to Prometheus / Grafana.

rsc_webui – lightweight FastAPI dashboard for Δφ / Γ / lock status visualization.

rsc_alert_daemon – webhook alerting with threshold logic.

ismxlang.yaml – declarative configuration and policy definitions.

run_ismx.py – demo runner for local or simulated environments.

This version forms the public “shell” — safe to integrate, inspect and extend.
The confidential core is injected as a black-box module during internal builds.

🧪 Current Development Stage (October 2025)
Area Status Description
Architecture ✅ Stable

Modular, tested in local and simulated environments Runtime Logging ✅ Complete

JSONL + checksum + AES-GCM optional Prometheus / WebUI ✅ Functional Live metrics, Δφ / Γ visualization Core Model (Γ–Δφ) 🔒 Confidential Validated prototype, not publicly released Industrial Testing 🔄 In progress

Preparing MVP deployment for AI-Ops systems Security & Audit ✅ Implemented
No PII, hash-salted IDs, audit-ready rotation Collaboration 🟢 Open Seeking research & engineering partners

🚀 Why It Matters

Modern AI agents can lose internal coherence without realizing it.
Our framework adds:

self-monitoring capability – detect drift before failure,

adaptive gating – pause, reflect, or reduce output when unstable,

observability layer – operators see agent “health” in real time,

secure audit logs – verifiable, integrity-checked data trail.

It’s like a runtime nervous system for AI — lightweight, explainable, and safe.

🤝 Collaboration Invitation

We’re currently looking for:

AI/ML engineers with interest in runtime observability or agent orchestration,

research groups exploring autonomous stability and reflective control,

industry partners who want to integrate stability monitoring into AI-Ops or agentic frameworks.

Demo files:

Demo Light: https://drive.google.com/drive/folders/12PE-02hwDkm9nccfiUG9bZSZhERE6oxJ?usp=sharing

Demo with Docker: https://drive.google.com/drive/folders/1WaFSJwG-Yhha5bzpgujIrkl8B2CVMJWE?usp=sharing

You can reach out to discuss collaboration, private demonstrations, or closed technical audits.

📧 Contact: [e-mail]
(Please include “RSC Collaboration” in subject line.)

🔒 Legal and IP Notice

The concepts, architecture, and partial implementations described here are protected by copyright © 2025 Damjan Žakelj.
Core algorithms, numerical transforms, and stability mappings (Γ, Δφ) are proprietary trade secrets.
Publication of this article constitutes defensive prior art against external patenting of identical methods.

Public components are released under the Creative Commons BY-NC-SA 4.0 license.
Commercial use requires written permission.

📜 Summary

ISM-X / RSC Stack represents a new category of runtime layer for AI agents:
a minimal, auditable, security-aware system that quantifies coherence and drift in real time.

The architecture is public.
The mathematics is protected.
The door for collaboration is open.