Skip to content
Apart Research
Apart Research
Contact us
Sprint projectSep 14, 2026

What We Would Not See: Structural Blind Spots in the Public Record of Agentic AI Incidents

Guillem Bas · Team AI Risk Explorer (AIRE)

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

Read the report

Report: What We Would Not See: Structural Blind Spots in the Public Record of Agentic AI Incidents

Open in a new tab (opens in new tab)
Share

The public record of agentic AI incidents reflects detection capacity and disclosure decisions as much as events. From a corpus of 283 incidents, we cluster the 16 with real operational impact into six classes and compare them against the rest, surfacing five candidate blind spots. Each has behavioral precedents 16.9 to 28.4 months old, no substantially absent occurrence precondition, and visibility barriers that could suppress reporting. We conclude that absence is not straightforwardly evidence of non-occurrence.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

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. To me, this is novel work that has not yet been attempted with this amount of rigor -- great work dissecting and classifying incidents. Your efforts here can help shape the way people think about the fast-paced incidents happening around us.

  2. The main point is significant. The public record indicates what was detected and reported, but not necessarily what actually happened.

    What concerns me most is that the selection of scenarios maybe biases in the result. I would also back-test the framework across the six outcome classes using information only available at the time the outcomes were realsied, the scenarios were compliant with the operational working definition, and therefore acceptable.

Cite this project

@misc{bas2026we,
  title = {{What We Would Not See: Structural Blind Spots in the Public Record of Agentic AI Incidents}},
  author = {Guillem Bas},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/what-we-would-not-see-structural-blind-spots-in-the-public-record-of-agentic-ai-incidents-ocry}},
  url = {https://apartresearch.com/sprints/projects/what-we-would-not-see-structural-blind-spots-in-the-public-record-of-agentic-ai-incidents-ocry}
}

More from AI Incident Response Sprint

All 233 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026