hack with Hyd 2.0

A developer built SupportMind, a customer support agent with persistent memory and intelligent routing. The system uses Hindsight for session memory and cascadeflow for query routing, reducing costs from ~$0.012 to ~$0.002 per query by handling 80% of queries with cheaper models.

Support bots that forget every conversation aren't support bots. They're expensive FAQ pages. I built SupportMind to fix that — a customer support agent that actually remembers. The architecture is two layers: Memory Hindsight : After every interaction, the agent stores structured context in a vector namespace per user. Next session, it recalls semantically — "payment problem" retrieves "Visa charge failing" even if the words don't match. Routing cascadeflow : Not every query needs GPT-4. Password resets go to Groq's free tier. Complex billing disputes escalate. Every decision is logged with model, cost, latency, and reason. The delta that matters: Session 1: "Can you tell me your card details and the error you're seeing?" Session 3 same user, same issue : "I see you've had recurring issues with your Visa ending in 4242. Last time, clearing billing cache fixed it — want to try that first?" Same infrastructure. Completely different agent. On a typical support workload: ~80% simple queries handled by the cheap model. Cost per query dropped from ~$0.012 to ~$0.002. The part I didn't expect: routing and memory compound. When Hindsight shows a user has had the same issue four times, cascadeflow automatically classifies their next message as complex — even without explicit signals. That fell out of the architecture. 👇 https://lnkd.in/gn8NwP6Z https://lnkd.in/gn8NwP6Z hashtag AIAgents hashtag AgentMemory hashtag Hindsight hashtag cascadeflow hashtag LLM hashtag AI