R0041/2026-04-01/Q002/SRC06

Research	R0041 — Enterprise Sycophancy
Run	2026-04-01
Query	Q002
Search	S04
Result	S04-R03
Source	SRC06

XMPRO analysis of sycophancy in industrial multi-agent AI systems

Source

Field	Value
Title	When AI Agents Tell You What You Want to Hear: The Sycophancy Problem
Publisher	XMPRO
Author(s)	Pieter van Schalkwyk
Date	2025-06-03
URL	https://xmpro.com/when-ai-agents-tell-you-what-you-want-to-hear-the-sycophancy-problem/
Type	Industry analysis

Summary

Dimension	Rating
Reliability	Medium
Relevance	Medium-High
Bias: Missing data	Some concerns
Bias: Measurement	N/A
Bias: Selective reporting	Some concerns
Bias: Randomization	N/A -- not an RCT
Bias: Protocol deviation	N/A -- not an RCT
Bias: COI/Funding	High risk

Rationale

Dimension	Rationale
Reliability	Industry practitioner with domain expertise in industrial AI; however, XMPRO is a vendor
Relevance	Provides the only multi-agent sycophancy scenario found for industrial/manufacturing contexts
Bias flags	XMPRO sells industrial AI solutions and has incentive to highlight problems their products address. Scenarios may be hypothetical rather than documented incidents

Evidence Extracts

Evidence ID	Summary
SRC06-E01	Multi-agent sycophancy in manufacturing: agents adjusting anomaly assessments to match consensus, missing critical safety issues