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Judging every step of an AI agent with a second LLM often costs more than the agent itself.

This judge's tax doubles your token spend and slows down response times for every single task.

Most people think the only way to ensure agent reliability is to have another AI monitor the output. What actually happens is a massive waste of compute on semantic judging when the answer is already in the telemetry.

Telemetry is the raw digital footprint of the system.

Instead of asking an LLM if the agent is looping, you look for three identical consecutive tool calls.

Instead of asking if the agent failed, you track the 403 error code from the API.

This turns a costly, slow review process into a binary trigger. When a signal hits, you inject a recovery prompt that tells the agent exactly what failed and how to pivot.

I condensed the technical setup into a visual field guide — swipe through below.

The full breakdown of the telemetry signal chain is in the 12-page guide.

How are you currently detecting when your agents drift off goal or enter an infinite loop?

#YourBrand #LLMOps #AIAutomation #BusinessAnalysis #WorkflowAutomation