Expired50% confidenceFactExact time
Current tool selection strategies rely heavily on the model's immediate judgment and lack systematic optimization; in enterprise scenarios where available tools can number in the tens to hundreds, models are prone to hallucination, repeated calls, or selecting the wrong tool.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/22/2026
First Seen
Valid until: 10/20/2026(expired)
Sources
Related Claims
UnverifiedWhen an agent is given 100 tool descriptions, the model experiences difficulty choosing and may even hallucinate non-existent parameters, causing the invocation accuracy rate to drop off a cliff.74% similarUnverified企业级落地中工作流的健壮性设计(错误重试、异常兜底、人工介入节点)往往比功能实现本身更为关键73% similarUnverified这些模型版本管理工具的共同痛点是依赖团队主动、规范地记录元数据,高压迭代下工程师往往省略这一步骤72% similarUnverifiedThe core challenges of large model applications often lie not in the model itself, but in prompt engineering optimization, hallucination handling, multi-turn conversation state management, and integration with existing enterprise systems.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/590011API
curl https://kongchang.com/api/v1/knowledge/claims/590011MCP
get_claim(id=590011)