Source record

Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2607.14275.

Pipeline notes

The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.

Paper validates context-engineering quality as an independent leading indicator of AI agent reliability. Using ProofAgent-Harness, it measures context across seven criteria and shows through controlled studies that context-quality scores predict specific behavioral outcomes including hallucination resistance, manipulation resistance, instruction following, and tool use.

Offers a concrete, validated seven-criterion preflight framework for agent reliability with open-source tooling, backed by controlled experiments isolating context quality from model behavior.

Cited by

• Safe models do not compose into safe systems