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Sprint projectSep 14, 2026Saarbrücken

What’s in the Black Box? Cognitive-Behavioral Forensics of Autonomous AI Agents

Agnes Ahalya Arogyaraj · Team A2J

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

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Report: What’s in the Black Box? Cognitive-Behavioral Forensics of Autonomous AI Agents

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This work proposes a theoretical framework for forensics analysis that combines task-bound capability authorization with behavioral conformance

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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. This work does a good job of separating risk scoring from continuous monitoring and shows why looking at the full sequence of agent actions can provide more information than checking individual events. I also liked that the paper is careful about the early-warning results. The main limitation is that the current testing uses constructed scenarios and example thresholds. Testing with larger real-world datasets, realistic false-positive rates, and actual response times would help show whether this approach performs better in practice.

Cite this project

@misc{arogyaraj2026whats,
  title = {{What’s in the Black Box? Cognitive-Behavioral Forensics of Autonomous AI Agents}},
  author = {Agnes Ahalya Arogyaraj},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/whats-in-the-black-box-cognitivebehavioral-forensics-of-autonomous-ai-agents-ut6w}},
  url = {https://apartresearch.com/sprints/projects/whats-in-the-black-box-cognitivebehavioral-forensics-of-autonomous-ai-agents-ut6w}
}

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