Show HN: Morph Reflexes – Multi-head classifiers for agent traces

# Show HN: Morph Reflexes – Multi-head classifiers for agent traces Source: [https://news.ycombinator.com/item?id=48739038](https://news.ycombinator.com/item?id=48739038) The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more\. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale\. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API\. Built on custom kernels and a custom inference engine forked from vLLM\. Under the hood, it is a small LLM architected around multi\-head inference\. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes no sense\. How it works: We use a modern LLM with hybrid attention and remove the decode step\. We built an inference engine that lets prefill compute be 99% reused from reflex to reflex, similar in spirit to older 2019\-era BERT/HYDRA and older multiple\-head techniques\. we built the inference engine to reuse the KV/cache across inputs and compute across all reflexes\. One shared backbone reads the trace once, then many heads classify different signals\. Our inference engine reuses the same KV/cache and compute across all reflexes, giving us sub\-30ms inference with less than 0\.1% overhead for each additional reflex\. We took the same high\-level idea and did the hard work to make it work with a modern architecture and attention\. On it, we can run inference in under 30ms and serve the full request in under 90ms\. If you run 4 reflexes or 100, the extra overhead is less than 2ms\. Why does optimizing this matter? If you’re even a medium\-sized startup, you’re dealing with tens of thousands of agent runs and millions of turns\. If you want to track things like user frustration rates over time, frontier LLM\-as\-judge does not scale\. I built a similar stack at Tesla\. When ML engineers needed to sample data across petabytes for signals like \`is\_camera\_obfuscated=true\`, along with 200 other things, you need to 1\) spin them up quickly 2\) run at scale efficiently What it is not: A dashboard\. 99% of dashboards go unused\. 100% API first and made for devs who want to use this to trigger their own stuff\. vibetrain a custom reflex in our dashboard, and/or then let it self improve in production:[https://www\.morphllm\.com/dashboard/reflex](https://www.morphllm.com/dashboard/reflex) Docs:[https://docs\.morphllm\.com/sdk/components/reflexes/index](https://docs.morphllm.com/sdk/components/reflexes/index) I’d love feedback from people running agents in prod: what sorts of things do you wish you could track over time across 100% of turns but cant right now? TLDR: semantic signals from agent traces, super fast, cheap via API