A new approach called Outcome Monitors has been proposed to detect tool failures and provide recovery options for agents. When a tool call times out, the agent can route around it or consume a cached error page. Outcome Monitors detect violations of outcome contracts mined from task-disjoint traces or public schemas, preserving results and issuing non-binding receipts with recovery tools. In experiments, this approach improved completion rates by up to 28.1% in frozen evaluations with injected failures. --- Why it matters: This matters because it addresses a common issue in AI systems: tool failures can cause entire workflows to fail. Outcome Monitors provide a way to detect and recover from these failures, which is crucial for reliable and efficient AI operations. Source: https://arxiv.org/abs/2608.19303