AI agents are wasting tokens on repeated work. I built something to fix it and need testers.

While building AI-agent workflows, I kept running into the same problem: agents repeatedly process similar context and burn tokens on work that's already been done before. To address that, I've been building a system that reuses useful information across workflows instead of repeatedly sending the same context back to the model. Early testing shows significant token savings in repeated-context scenarios, but I need real-world feedback to understand where it works, where it fails, and what use cases I'm missing. If you actively build AI agents, automations, or LLM-powered products, I'd appreciate honest feedback. The project is currently free to test. You can find it in my profile or by searching "truvasocial" on google. I'm not looking for customers right now—I'm looking for builders willing to stress-test it and tell me what breaks.