Behavioral observability for AI agents, built and priced to run on millions of production turns.

Most agent failures don’t show up as errors.

The API returns 200.
The tool call succeeded.

But the agent is looping. The user is frustrated. A jailbreak slipped through. The answer technically completed, but the experience failed.

That is the problem Reflexes solves.

Reflexes are fast classifiers that run on every turn of every agent conversation and catch the behavioral signals normal observability misses:

• user frustration
• infinite loops
• jailbreak attempts
• refusals
• hallucinations
• task failures
• policy violations
• custom signals specific to your product

Traditional observability tells you when software breaks. Reflexes tells you when the agent experience breaks.

The reason this has not worked before is cost and latency. Frontier-model LLM-as-judge is fine for offline evals, but it is too slow and expensive to run across production traffic.

Reflexes are built and priced to run at scale: millions of turns, inline, across 100% of production conversations.

Under the hood, Reflexes uses a shared backbone with many small heads, so multiple behavioral signals can run on the same conversation without multiplying latency or cost.

You can use our default reflexes out of the box, or train a custom reflex for the failure modes that matter to your agent.

We built this after working with production agent teams and seeing the same pattern repeatedly: agents usually fail long before they throw an error.

They fail in the behavior.

Try Reflexes: https://www.morphllm.com/products/reflex