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152 points Gaishan | 2 comments | | HN request time: 0.412s | source
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simonw ◴[] No.45341827[source]
I went looking for how they define "agent" in the paper:

> AI agents are autonomous systems that can reason about tasks and act to achieve goals by leveraging external tools and resources [4]. Modern AI agents are typically powered by large language models (LLMs) connected to external tools or APIs. They can perform reasoning, invoke specialized models, and adapt based on feedback [5]. Agents differ from static models in that they are interactive and adaptive. Rather than returning fixed outputs, they can take multi-step actions, integrate context, and support iterative human–AI collaboration. Importantly, because agents are built on top of LLMs, users can interact with agents through human language, substantially reducing usage barriers for scientists.

So more-or-less an LLM running tools in a loop. I'm guessing "invoke specialized models" is achieved here by running a tool call against some other model.

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1. eric-burel ◴[] No.45343430[source]
LLM running tools in a loop is the core idea of ReAct agents, and is indeed one of the most effective way to extract value from a generative AI. Ironically, it's not about generation at all, we use the models classification skills to pick tools and text processing skills to take the context into account.
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2. ijk ◴[] No.45347558[source]
I tend to find that using LLMs for interpretation and classification is often more useful for a given business task than wholesale generation.