Unverified50% confidenceFactExact time
The main reasons Agent frameworks are slower than direct conversation are that system prompts inject large numbers of tool definitions, multi-round ReAct reasoning loops occur, and context accumulation causes the number of input tokens to surge.
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50%
Confidence
Long-term
Relevance
8/24/2026
First Seen
Sources
本地AI Agent部署太慢？轻量级优化实战指南
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Related Claims
UnverifiedAs multi-turn tool calls and conversations accumulate in Agent tasks, context expansion causes inference costs to rise exponentially and response latency to increase78% similarUnverifiedLong-horizon agent tasks rely on reasoning-action loops such as the ReAct framework, where errors cascade and amplify along the chain, causing completion rates to fall far below those of single-step question answering.73% similarUnverifiedIn long contexts, models are more easily influenced by the most recent few turns of conversation rather than the system prompt set at the beginning, a phenomenon known as instruction-following decay.73% similarUnverifiedAgent交接时传递上下文过多会增加Token消耗和延迟，传递过少则导致接手Agent缺乏关键背景73% similarUnverifiedOverly verbose, contradictory, or ambiguous system prompts cause the model's attention to become scattered, resulting in over-thinking or instruction-following failures; streamlining agents.md can improve the determinism of instruction following.72% similar
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