How are people reducing token waste in AI agent workflows?

Reddit r/AI_Agents 06/10/26, 08:02 AM Tools

token-waste ai-agents context-deduplication open-source proxy caching model-routing

Summary

Discusses token waste in AI agent workflows due to repeated context, introduces an open-source proxy called Badgr-auto for deduplication, and asks the community how they handle the issue.

I’ve been running into a recurring issue with AI agents, they resend a lot of the same context over and over. Same files, same code blocks, same conversation history, same structured context. Over longer sessions, the token usage gets pretty wasteful. I’m working on an open-source local proxy called Badgr-auto that sits between the agent and the model and removes safe duplicate context before the request is sent. The tricky part is deciding what should never be touched: system prompts tool calls tool results latest user message long natural-language prompts For people building or running agents in production, how are you handling this? Are you deduping repeated context, summarizing history, using model routing, caching, or just accepting the extra token cost?

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