What question did this study set out to answer?

The aim is to develop a framework that allows autonomous AI agents to operate securely and accountably in high-stakes environments.

February 12, 2026Open Access

TRACE: Trusted Runtime for Autonomous Containment and Evidence

View Full Paper

Ask AI

Bookmark

The aim is to develop a framework that allows autonomous AI agents to operate securely and accountably in high-stakes environments.
Introduced TRACE as a governance-first execution framework for AI agents.
Implemented cryptographically signed policy bundles for operations.
Defined assurance properties and proof obligations related to mediation and evidence invariants.
Developed an Interface Gateway for complete mediation of actions.
Created LLM-specific tripwire metrics for operational monitoring.
Established ten assurance properties with specified dependencies and proof obligations.
Developed a signed evidence log with timestamps for audit and forensics.
Introduced graduated containment levels for different operational risks.
Released a reference implementation of core algorithms as a starting point for further development.

No takes yet. Share an insight, caveat, or question.

Governance framework enhances autonomy in AI agents, indicating new measures for assurance and accountability.

Elias Calboreanu (2026) studied this question.

View Full Paper

Ask AI

Bookmark