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Core module of Sentinel OS - an offline-first, memory-persistent AI operating core for autonomous secure agents in zero-trust environments.

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Sentinel OS – Core Architecture

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Sentinel OS is a modular, offline-first cognitive operating core for synthetic intelligence systems. Built to function without cloud dependence, it enables secure autonomous reasoning, persistent memory, and introspective goal evolution in any environment — even adversarial or air-gapped.

🔗 Live Preview: https://sentinel-os.netlify.app


Project Status

This repository is under active development. All source code, models, and architectural modules are being incrementally built and tested in alignment with funded research objectives. The first implementation focus is the Autonomic Distributed Cognition System (ADCS), submitted for NSF and IQT review.


Core Features

  • Belief Ecology Engine – Self-regulating cognitive memory structure with contradiction tracing.
  • Recursive Contradiction Tracer – Identifies and logs internal conflicts across memory and reasoning chains.
  • Goal Collapse Engine – Manages conflicting goal trees and adapts behavior accordingly.
  • Local LLM Interface – Operates without cloud APIs. Supports lightweight local model execution and response generation.
  • Memory Layer (Persistent) – Structured episodic memory system with replay, mutation, and filtering.
  • Graph Introspection Module – Visualizes internal logic nets, contradictions, and goal evolution over time.
  • Secure Sandbox Layer – Prevents unauthorized memory overwrites and locks critical internal operations.

Architecture

Sentinel OS is a modular, offline-first AI reasoning system built to operate autonomously, securely, and transparently. It includes:

  • Input Layer – Accepts commands or sensor data (via CLI/API)
  • Memory Layer – Persistent + episodic storage with replay/mutation
  • Belief Ecology Engine – Maintains internal truth, detects contradictions
  • Contradiction Tracer – Identifies logical conflicts for self-debugging
  • Goal Collapse Engine – Dynamically prioritizes or drops goals
  • Security Layer – Prevents logic corruption or unsafe rewrites
  • Local LLM Interface – Optional lightweight GPT-style model (runs offline)
  • Graph Engine – Visual introspection of beliefs and memory clusters
  • Output Layer – Executes commands or exports reasoning results

📊 System Diagram

Sentinel OS Architecture


Why It Matters

Most AI systems today are fragile, cloud-dependent, and unaware of their contradictions. Sentinel OS flips that paradigm.

It is built to:

  • Operate fully offline, in closed-loop or hostile environments.
  • Diagnose its own logical faults through contradiction tracing.
  • Mutate memory and goals without external prompts.
  • Support secure, autonomous agents in defense, research, or disaster response.

Pitch + Docs

📄 Download Full Pitch Deck (PDF)
🌐 Visit the Official Landing Page


Development Phases

  1. Phase 1 – ✅ Completed:

    • Initial architecture and subsystem planning.
    • NSF and IQT submissions with detailed system blueprint.
  2. Phase 2 – 🚧 In Progress:

    • Core modules under active development.
    • Local introspection and persistent memory layer nearing first working prototype.
  3. Phase 3 – 🔜 Scheduled:

    • Real-world testing in isolated containers.
    • Offline deployment simulation and adversarial condition trials.

Licensing

This repository is covered by a custom All Rights Reserved license.
No derivative works, redistribution, or commercial usage is permitted without explicit written permission from the author.


Author

Bradley R Kinnard
Aftermath Technologies
📧 [email protected]
🌐 https://aftermathtech.com

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Core module of Sentinel OS - an offline-first, memory-persistent AI operating core for autonomous secure agents in zero-trust environments.

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