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.

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 + older multiple-head techniques.

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. In my experience, 99% of dashboards go unused. This is purely API-based and made for devs who want to track agent behavior themselves and trigger their own alerts and build on it.

You can vibetrain a custom reflex in our dashboard, and then let it self improve in production: https://www.morphllm.com/dashboard/reflex

Docs: 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?

TLDR: semantic signals from agent traces, super fast, cheap via API

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