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Sprint projectSep 14, 2026Shenzhen

RealityGate: An Executable, Auditable Containment Standard for Guardrails-Off Agent Evaluations

Xiwei Chen · Team Sunny

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: RealityGate: An Executable, Auditable Containment Standard for Guardrails-Off Agent Evaluations

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RealityGate is an executable, auditable containment standard for guardrails-off agent evaluations: a deterministic ALLOW/HOLD/KILL gate, one-shot capabilities, and an egress gateway turn “was this contained?” into checkable clauses with exportable evidence. In 39 automated tests and nine live-model runs, no unauthorized external effect occurred; in one informed gate-test a model proposed prohibited exfiltration and the gate killed the run.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. The testing is limited to just nine live-model runs across four model families. More evaluations could provide better metrics for the paper. The collusion detection is derived from a single public-wiki dataset and remain vulnerable to keyword evasion.

  2. RealityGate turns containment rules into an inspectable prototype with action checks, tests, and exported audit records. The report clearly separates its mocked environment from a production deployment and records how the model probes behaved. Permission tickets are checked after execution, and external stopping is not synchronized with the active run, which creates some limitations with the current claims. Even within that scope, the project makes the proposed controls concrete enough to examine and compare.

Cite this project

@misc{chen2026realitygate,
  title = {{RealityGate: An Executable, Auditable Containment Standard for Guardrails-Off Agent Evaluations}},
  author = {Xiwei Chen},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/realitygate-an-executable-auditable-containment-standard-for-guardrailsoff-agent-evaluations-nq2j}},
  url = {https://apartresearch.com/sprints/projects/realitygate-an-executable-auditable-containment-standard-for-guardrailsoff-agent-evaluations-nq2j}
}

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