RESEARCH / AUTONOMOUS SYSTEMS

An agent earns trust through work people can inspect.

Moving from a prompt to a real outcome takes more than fluent language. It takes planning, useful tools, feedback, deliberate permissions, and a reliable way to tell whether the result is right.

01 / KEY QUESTIONS

The central question.

AUTONOMOUS SYSTEMS

How can agents work over long tasks without losing the original goal?

Questions that follow

  • How should external actions be authorized, observed, and reversed?
  • Which checkpoints give people the clearest view of progress and risk?

How we study it

A useful evaluation follows observable decisions and changes: task criteria, tool results, permission boundaries, recovery from errors, and the evidence a person needs to review the outcome.

What meaningful progress looks like

Measure successful completion, recovery after failure, adherence to permissions, and the human effort needed to review or correct the result.

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02 / SELECTED SOURCES

Read the work behind these questions.

These independent papers and official guidance offer useful starting points for this field. Follow each link to review the original methods, evidence, and limits in context.

PRIMARY RESEARCH & OFFICIAL GUIDANCEYao et al., “ReAct: Synergizing Reasoning and Acting in Language Models” (2022) ↗Liu et al., “AgentBench: Evaluating LLMs as Agents” (2023) ↗

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See how the questions connect.

Read our vision ↗