Unverified50% confidenceFactExact time
现代基于LLM的Agent通过自然语言角色描述和工具调用能力获得自主执行任务的能力，无需手工编程定义行为逻辑
1
Sources
50%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
Claude Code in Practice: Writing Maintainable AI Code with the Harness Methodology
bilibili图灵诸葛官方号6/9/2026
Related Claims
UnverifiedLLM兴起后，多智能体系统中的Agent从刚性规则执行器转变为具备自然语言理解与生成能力的柔性智能体，能够动态调用外部工具81% similarUnverifiedCombining LLMs with tool calling, memory mechanisms, and planning capabilities forms an Agent with autonomous action capabilities.81% similarUnverifiedAfter the rise of large language models, each Agent possesses natural language understanding and generation capabilities, enabling it to dynamically interpret task instructions and invoke external tools.79% similarUnverified智能体的许多能力实际上是由Agent所运行的框架在LLM外部实现的，而非LLM本身79% similarVerifiedAn Agent is an application-layer architecture built on top of an LLM that enables the model to decompose complex tasks, invoke external tools, maintain context state, and iteratively optimize results by introducing mechanisms such as Planning, Memory, Tool Use, and Reflection.78% similar
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