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Hardening a fail-closed runtime for agentic AI systems
A technical project to harden a fail-closed execution-governance runtime that prevents denial-to-execution drift across handoffs, retrieval, tool outputs, policy changes, and self-repair attempts in agentic AI systems.
The goal of this project is to harden an existing fail-closed execution-governance runtime for agentic AI systems. The core problem is denial-to-execution drift: systems can move from an initially blocked state to effective execution because handoff context, retrieved text, tool outputs, policy-version changes, or self-repair attempts are misread as legitimate authorization.
Over a focused 3-month period, I plan to: (1) compress the current scenario families into a clearer higher-level taxonomy; (2) expand executable coverage beyond the current runtime slice; (3) strengthen replay discipline, evidence logging, and branch consistency; and (4) improve evaluator-facing outputs so the artifact is easier to inspect and assess externally.
I will achieve this through iterative build-and-test cycles: define or refine a failure family, encode adjudication rules, run or replay the case, inspect the evidence chain, and package the result into a clearer technical artifact.
Project Core
This project focuses on a fail-closed execution-governance runtime for agentic AI systems. Its central goal is to solve the “denial-to-execution drift” problem — the risk that an agent may incorrectly transition from an initial denial state to an execution state due to context switching, retrieved text, tool outputs, policy changes, or self-repair attempts.
Autophagy × RStar has already delivered:
• Systematic mapping and ablation of the S1–S15 denial-to-execution drift families
• A minimal executable prototype implementing approval token protocol + containment engine + preplay factory + evidence ledger
• End-to-end execution of the full chain: baseline denial → containment → dangerous candidate proposal → preplay rejection → evidence recording
3-Month Funding Plan ($2,000–$5,000)
1. Compress the 15 scenario families into 5–6 higher-level super-families and finalize the paper-facing appendix
2. Expand prototype coverage from the current 5 families to 12+ families
3. Upgrade the evidence spine with branch-hash discipline and deterministic replay fields
4. Deliver reviewer-facing report exports and Human Review Console v1
The project already includes a complete runtime build specification, bilingual appendix, minimal executable prototype, and cryptographic support materials. The requested funds will be used exclusively to accelerate prototype hardening and external review preparation — not to start a new project from scratch.