Abstract
LungClaw introduces deterministic metabolic governance for autonomous agentic systems. The architecture treats execution capacity as a finite, governed resource and validates every transition against explicit constraints before an action can consume it. This separates probabilistic reasoning from deterministic execution control.
The framework combines atomic energy commits, monotonic state evaluation and fail-safe interruption. Its invariants prevent retry amplification, unbounded loops and cross-agent escalation while preserving the ability of agents to plan and reason flexibly.
Core contributions
- Constraint-based control: every state transition is validated against explicit, reproducible rules.
- Bounded execution cost: computational activity consumes a governed budget that cannot be silently exceeded.
- Non-adaptive containment: safety boundaries do not weaken in response to model behavior.
- Atomic energy commits: resource accounting and state changes are committed as a single deterministic operation.
- Fail-safe interruption: invalid or exhausted execution paths terminate predictably.
Publication record
This page is the author-maintained research record. The versioned preprint is archived on Zenodo and the public white paper is available from the LungClaw project. A contextual introduction is published by Enkronos.
| Record | Purpose | Identifier |
|---|---|---|
| Version DOI | Immutable citation for version 1.0 | 10.5281/zenodo.18704803 |
| Concept DOI | Persistent identifier across future versions | 10.5281/zenodo.18704802 |
| LungClaw white paper | Project publication and downloadable paper | lungclaw.com |
| Enkronos introduction | Editorial context and project overview | content.enkronos.com |
How to cite
Busato, G. (2026). LungClaw: Deterministic Metabolic Governance for Autonomous Agentic Systems (Version 1.0). Zenodo. https://doi.org/10.5281/zenodo.18704803
BibTeX
@misc{busato2026lungclaw,
author = {Busato, Gianluca},
title = {LungClaw: Deterministic Metabolic Governance for Autonomous Agentic Systems},
year = {2026},
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.18704803},
url = {https://doi.org/10.5281/zenodo.18704803}
}