From Evidence to Action: How Tool-Using Agents Fail
Quick summary
arXiv:2610.07753v1 Announce Type: cross Abstract: Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence established beforehand. We study where this evidence-to-action chain breaks as agents move from deciding whether to act to executing single actions and dependent workflows. Across ten model-harness configurations, strong static action assessment can coexist with much weaker interactive execution. Failures often begin before execution: agents stop with incomplete investigation or act before required
Key takeaways
- arXiv:2610.07753v1 Announce Type: cross Abstract: Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence established beforehand.
- We study where this evidence-to-action chain breaks as agents move from deciding whether to act to executing single actions and dependent workflows.
- Across ten model-harness configurations, strong static action assessment can coexist with much weaker interactive execution.
Why it matters
“From Evidence to Action: How Tool-Using Agents Fail” is a product decision that may change how people work with AI. Its value depends on task completion, correction effort and data handling—not simply the presence of a new feature.
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