Verified65% confidenceFactExact time
Bugs in AI products are often not crash errors but outputs that appear reasonable yet are actually incorrect—so-called silent failures—which are harder to catch and more destructive.
3
Sources
65%
Confidence
Long-term
Relevance
9/28/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedFailures in AI agents are often semantic deviations rather than explicit error codes, and this kind of "successful failure" requires the orchestration layer to have semantic-level error recognition capability.80% similarUnverifiedFor APIs aimed at AI agents, silent failure is especially dangerous, because agents do not try multiple scanning methods or report 'can't scan it' like humans do; they simply return an empty result, making it hard for upstream to locate the root cause.80% similarUnverifiedThe most troublesome problems in multi-step AI systems are often cases where execution technically succeeds but the result is completely wrong, which do not cause the system to report errors.78% similarUnverifiedAI customer service deployment failures typically do not manifest as errors or outages, but as the agent confidently making incorrect commitments.78% similarUnverifiedAI系统的静默失败表现为技术层面健康(返回200状态码、日志无报错)但业务质量持续下滑78% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/956819API
curl https://kongchang.com/api/v1/knowledge/claims/956819MCP
get_claim(id=956819)