Agent Data

Agent data is Alexandr Wang’s term in Alexandr Wang on Scale and AI Data Infrastructure for data about how people complete tasks, not just what final answers look like. He describes the needed data as traces of thinking, information gathering, constraint checking, decision-making, and action.

The concept is tied to the shift from chatbots to agents. If AI systems move from talking to doing, then AI Data Infrastructure must capture how capable people actually perform work such as booking flights, reviewing contracts, building software features, or making product decisions.

Key Claims

• Agent data is process data, not only input-output pairs.
• Many valuable workflows lack good training data because the reasoning, checking, and tool-use steps are not captured.
• Agent data depends on human experts because models still hallucinate, get stuck, and need guidance in real-world domains.
• Capturing agent data could make Data As Education more concrete by turning expert task performance into teachable sequences.
• The data is valuable only if privacy, permissions, task context, and evaluation are handled carefully.

Connections

• Alexandr Wang and Scale AI - source person and company.
• AI Data Infrastructure, Data As Education, and Data Engine Learning Loop - data concepts this extends.
• Agentic Workflow, Human-Agent Collaboration, and Human Judgment Under AI - workflow and oversight context.
• Context Engineering and Persistent Agent Memory - adjacent context layers agents need while acting.