Introducing Morph Reflexes. AI observability is measuring the wrong thing. Agents fail long before they throw errors. They start looping. Users gets angry. Jailbreaks happen. Reflexes are fast, small models that catch signals in agent convos and run on 100% of prod

Frontier-model LLM-as-judge works for offline evals, but it’s too slow and expensive to run on every turn. Reflexes detect behavioral signals at scale like frustration, loops, jailbreaks, and confusion. Track them over time, across thousands or millions of conversations.

Reflexes are built to run on 100% of production amd run in under 90ms Add more Reflexes to the same request with near-zero additional latency How? We utilize shared backbone, multi head inference with a modern LLM

LLM-as-judge works for offline evals. It does not work for every production turn. Reflexes are for companies that care about Agent Experience Use it in a Grafana dashboard, Slack app, CLI, MCP, etc... Realtime API starts at $0.00025/event Batch API starts at $0.00001/event

Need a custom reflex? Vibetrain one in our dashboard in under 30 minutes. Even if you have no dataset. Custom Reflexes can also self-improve in production (opt-in)

Docs: Try it in the playground:

when everyone relies on the same few model providers, dense feedback on your harness seperates good products from broken ones

Catching the loop before the error is what matters. The crash usually shows up late.

Congratulations!

Congrats!!

Interesting

totally clutch

this is so, so cool. morph stack 🐐

Docs: Try it in the playground:

Agent failures don't crash. They loop, drift, and silently degrade. Running behavioral oversight on every production conversation addresses the failure modes that error-based monitoring simply cannot see.

interesting angle, catching loops before they explode. the 90 ms target is tight-how do you handle model drift over time?

site down..

fixed