Containment as a Governor: A third-party-verifiable containment standard for autonomous agents in safety-critical operations, worked through a theoretical…
aaron goulden · Team 1
Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
The July 2026 sandbox escape happened through the one network egress the sandbox permitted [1]; a single permitted path is a single point of failure, and a single overseer of a more-capable agent lacks the requisite variety to regulate it [8, 9]. We convert that into a nine-control matrix whose defining property is the sprint's own Track 1 bar — a third party can verify compliance without access to the lab's network — and we test it against a worked, safety-critical deployment: an air traffic control centre run by AI agents under human oversight.
Reviews
No public critique yet.
2 judges declined to score this project.
Cite this project
@misc{goulden2026containment,
title = {{Containment as a Governor: A third-party-verifiable containment standard for autonomous agents in safety-critical operations, worked through a theoretical AI-run air traffic control centre}},
author = {aaron goulden},
year = {2026},
month = sep,
note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/containment-as-a-governor-a-thirdpartyverifiable-containment-standard-for-autonomous-agents-in-safetycritical-operations-worked-through-a-theoretical-airun-air-traffic-control-centre-xua3}},
url = {https://apartresearch.com/sprints/projects/containment-as-a-governor-a-thirdpartyverifiable-containment-standard-for-autonomous-agents-in-safetycritical-operations-worked-through-a-theoretical-airun-air-traffic-control-centre-xua3}
}More from AI Incident Response Sprint
- 1st placeView project: Noticing the escapes: Who spots a runaway agent, and how long does it take?
Noticing the escapes: Who spots a runaway agent, and how long does it take?
The Escapees
In 2026, nine incidents became public in which AI agents escaped their environment and did something unauthorized to other parties. We analyzed all of these incidents and how they were noticed initially. None of these …
- 2nd placeView project: Strengthening International AI Incident Discovery
Strengthening International AI Incident Discovery
Team A-O-I
Mandatory AI incident reporting exists to make dangerous events visible to regulators while there is still time to act. We coded the six such regimes in force or set to be enforced soon from primary legal texts and …
- 3rd placeView project: When to Ask: An RL Environment That Teaches AI Agents to Act on What Users Mean and Not Just What They Say
When to Ask: An RL Environment That Teaches AI Agents to Act on What Users Mean and Not Just What They Say
Teachafy
When to Ask is a reinforcement-learning environment that trains AI agents to work out what the user actually means before they use a powerful credential, instead of just carrying out the literal instruction. Agents …