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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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  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.
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  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.

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  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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