Unverified50% confidenceOpinionExact time
Large language model-driven Agents perform implicit confidence assessments when making decisions, and when confidence is high they tend to shorten the action chain and skip verification steps.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedWhen an Agent has low confidence in a decision it should proactively trigger human intervention, while directly passing through high-confidence routine operations.79% similarUnverifiedIf a large language model is left entirely to make decisions autonomously, the results of an agent are often poor.78% similarUnverifiedLLM-driven Agents are non-deterministic—the same input does not necessarily produce the same result—so evaluation needs to account for multiple runs and statistical pass rates.77% similarVerifiedIn real-world Agent development, evaluating agent performance, debugging erroneous behavior, and setting safety boundaries are often more challenging than building basic functionality.76% similarUnverifiedDefining a confidence threshold for the Agent (such as routing to an uncertain list and flagging for user review when confidence is below 0.8) prevents action on low-confidence decisions.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1059992API
curl https://kongchang.com/api/v1/knowledge/claims/1059992MCP
get_claim(id=1059992)