Unverified50% confidenceOpinionExact time
LLM Agent behavior is gradually generated by the model at runtime based on context, the risk of the same permission differs drastically across different contexts, and the combinatorial behavior space far exceeds any static enumeration capability.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLLM outputs are probabilistic—the same specification may produce drastically different behaviors across different runs—and therefore declarative specifications for AI Agents more closely resemble constraint declarations rather than state declarations.79% similarUnverifiedThe non-determinism of Agents stems from the token sampling mechanism of LLMs based on probability distributions, the dynamic changes in the context window, and silent updates to model versions.77% similarUnverifiedThe fundamental shift in Agentic LLMs lies in combining the reasoning capabilities of language models with the ability to act in real environments, transforming models from passive consultants into active agents.76% similarUnverifiedLLM jailbreaking refers to an attacker using carefully crafted prompts to induce a model to break through its built-in safety alignment or system instructions and perform actions that should be prohibited.75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1015933API
curl https://kongchang.com/api/v1/knowledge/claims/1015933MCP
get_claim(id=1015933)