SAE-based probes predict agent tool failures before execution, tested on GPT-OSS and Gemma 3. Adds internal observability missing from current external methods. Hariom Tatsat and Ariye Shater introduced SAE-based probes that predict agent tool failu...
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Mateo Ruiz
This is a really interesting direction because most agent observability today is reactive you find out something went wrong only after a tool has already been called.
A pre-execution risk signal could be especially useful in production workflows where tool calls are expensive or irreversible (payments, deployments, database writes, etc.). Even if the probe isn't perfectly accurate, using it as a trigger for additional validation or human approval on high-risk actions could reduce costly failures without slowing down every request.
I'd be curious to see how well this generalizes across different model families and real-world agent trajectories, since that's probably the biggest hurdle before it becomes part of production agent stacks.