Unverified50% confidenceFactExact time
Agent能力的底层依赖于大语言模型的推理能力（Chain-of-Thought）以及ReAct（Reasoning + Acting）框架
1
Sources
50%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Baidu AI Automation System: Trigger Skill Chains with a Single Sentence, Say Goodbye to Manual Workflows
bilibili俗人六哥_Ai电商实战7/7/2026
Related Claims
UnverifiedThe technical foundation for the shift to the Agent paradigm includes advances in large language models' reasoning capabilities, long-context processing, and Function Calling82% similarUnverifiedThe large language model capabilities behind Agent frameworks determine the quality of programming task completion more than the frameworks themselves.81% similarVerifiedAgent planning capabilities are typically implemented through the ReAct (Reasoning + Acting) framework.80% similarUnverifiedAgent系统通过ReAct、Chain-of-Thought等框架，让大语言模型在多轮循环中逐步分解和解决复杂任务80% similarUnverifiedAgents possess capabilities for planning, tool invocation, memory, and autonomous iteration, distinguishing them from the single-round question-and-answer mode of traditional large language models.80% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/492008API
curl https://kongchang.com/api/v1/knowledge/claims/492008MCP
get_claim(id=492008)