The Cost Trap of AI Agents

Author(s): AI Rabbit

Originally published on Towards AI.

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Have you ever wondered how multiple AI agents can interact seamlessly while keeping costs under control? When working with multiple AI agents in AutoGen, understanding and managing token consumption is critical to both cost optimisation and system performance. Let’s look at how tokens are consumed in different conversation patterns and explore some strategies for efficient token usage.

How do tokens add up in a simple interaction between you and an assistant?

Token accumulation:

First exchange: 250 tokens (100 input + 150 output)Second exchange: 200 tokens (50 input + 100 output)Total: 450 tokens

Isn’t it fascinating how each interaction builds on the previous one?

What happens when multiple agents join the conversation? Let’s have a look:

Token accumulation:

Initial task: 100 tokensAgent1 processing: 220 tokens (100 input + 120 output)Agent2 processing: 370 tokens (220 input + 150 output)Agent3 processing: 470 tokens (370 input + 100 output)Final result: 550 tokens (470 input + 80 output)

Can you see how quickly tokens can accumulate in a group chat scenario?

But How do you keep track of all these tokens?

AutoGen provides a middleware-based approach to tracking token usage. Let’s explore a token counter middleware implementation:

public class TokenCounterMiddleware : IMiddleware{ private read-only List<ChatCompletionResponse>… Read the full blog for free on Medium.

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Published via Towards AI