aperture

Aperture — See What Your Agent Actually Saw. AI agents often fail not because information is unavailable, but because critical evidence gets lost when execution histories are summarized or truncated. Inspired by a real engineering challenge documented by Raindrop, we built Aperture, an evidence-aware AI agent harness that investigates failures in multi-agent systems. Using the AEGIS dataset, Aperture starts with compact execution histories, selectively retrieves missing evidence, and verifies that every fault attribution references information actually delivered to the investigator. In one experiment, short context missed a faulty agent (F1: 0.667). Aperture recovered the missing evidence and correctly identified both faulty agents (F1: 1.0). Unlike ordinary retrieval systems, Aperture maintains an exact record of what the model received and enforces citation provenance through a deterministic gate outside editable retrieval policies. Our goal is to make agent investigations more reliable, transparent, and auditable. The prototype demonstrates evidence recovery and source verification, not general accuracy or cost improvements.