Unverified50% confidenceTradeoffExact time
AgentExecutor每次工具调用前会重新拼接完整对话历史进Prompt，导致Token消耗随任务步骤线性增长
1
Sources
50%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
LangChain实战入门：RAG应用与智能体开发全景指南
Related Claims
UnverifiedAgents repeatedly execute the perception-planning-action loop in cycles, and each round requires packing the complete history into the context window, causing input tokens to grow linearly or even exponentially.78% similarUnverifiedAgent Handoffs提升了准确率但每次交接都可能引入额外延迟，控制权转移需传递对话上下文并初始化新Agent系统提示词75% similarUnverifiedAgent systems need to manage cross-multi-turn dialog state and memory, error recovery and retry mechanisms, tool-call permission control and result caching, and concurrency race conditions—challenges that highly overlap with frontend asynchronous programming and state management knowledge.74% similarUnverifiedIn Agent scenarios, each tool call requires the full context to be re-input into the model, causing token consumption to grow exponentially.74% similar
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