Agents
NEW paper from Microsoft and colleagues. Debugging agent trajectories at scale is challenging. This is a clever approach to monitor and impr…
NEW paper from Microsoft and colleagues. Debugging agent trajectories at scale is challenging. This is a clever approach to monitor and improve agents in production. The problem: Finding which step in
NEW paper from Microsoft and colleagues. Debugging agent trajectories at scale is challenging. This is a clever approach to monitor and improve agents in production. The problem: Finding which step in a failed agent run caused the failure usually means one of two costly options. Run an expensive prompting pipeline over the whole trajectory, or post-train on failure data with step-level error labels that are hard to collect and difficult to scale. The solution: They propose OAT, a lightweight attributor that needs neither. It trains only on successful trajectories, models their dynamics with neural controlled differential equations, then flags the steps where a failure trajectory departs from that learned flow of success. Failure attribution becomes one-class learning over what success looks like, so you never need labeled error steps or failure data at all. Paper: https://arxiv.org/abs/2607.12747 Learn to build effective AI agents in our academy: https://academy.dair.ai/
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Source: DAIR.AI (X) | 2026-07-15