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# 🦹 Agent architectures | ||
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AI agents can consist of a single LLM as the decision maker or multiple LLMs (or same LLM, different prompts) working together to solve complex problems. | ||
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Let's look at a few different agent architectures: | ||
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## Tool-calling | ||
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This is the architecture most agents start with. It consists of a single LLM that has access to several tools to perform a range of tasks. | ||
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 | ||
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If you start with tool-calling agents, but upon thorough evaluation find that you need a more sophisticated architecture, only then consider multi-agent architectures. Bear in mind that fully autonomous multi-agent workflows mean higher costs, latency, and a system that is hard to debug, so use them with caution. | ||
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## Supervisor | ||
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In this architecture, a single agent (supervisor) interfaces with a group of agents to determine the next course of action. | ||
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 | ||
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## Network | ||
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In this architecture, each agent can communicate with every other agent and decide which one to call next or end the execution. | ||
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 | ||
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## Custom | ||
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In this setup, you can decide which agents can interact with each other and how the control flows between them. | ||
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 |
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