Expired60% confidenceFactExact time
After decomposition and planning, an Agent may trigger dozens of LLM inference calls, with cumulative token consumption reaching tens of times that of a direct question-and-answer scenario.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/7/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/5/2026(expired)
Sources
Agentic AI in Practice: Core Principles, Engineering Challenges, and Practical Recommendations
hackernewshackernews7/6/2026
Related Claims
UnverifiedDue to the cyclic reasoning nature of Agent systems, the complete conversation history is resent to the model after each tool call, causing Token consumption to grow super-linearly.77% similarUnverifiedMulti-step agents call large models far more times than single-turn question answering does, requiring techniques such as caching and tiered model invocation to optimize token consumption and response latency.75% similarVerifiedThe inference cost of large language models is variable and usage-based; a single Agent handling a complex task may trigger dozens of model calls and consume millions of tokens.74% similarVerifiedTop-tier models employ techniques such as chain-of-thought or extended reasoning, generating a large number of intermediate tokens during inference, causing per-call token consumption to be several times that of ordinary models.69% similarUnverifiedThe core logic of an Agent's memory mechanism is to save previous chat history and send it together with the next request to the large model.68% similar
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