Agents
A benchmark from Sierra that tests agents in multi-turn customer-service conversations, where a language-model-simulated user talks to an agent that has domain API tools and a written policy to follow.
A benchmark from Sierra that tests agents in multi-turn customer-service conversations, where a language-model-simulated user talks to an agent that has domain API tools and a written policy to follow. The original domains are retail and airline, and each run is scored by comparing the database state at the end of the conversation with the annotated goal state. The paper also introduced the pass^k reliability metric. A follow-up, tau2-bench, adds a telecom domain where the user also has tools and the agent must guide them.
Primary source: arxiv.org
Example
In practice, developers reach for tau-bench when they need the capability described above as part of an AI feature or workflow.
Hands-on guides, comparisons, and tutorials that cover Agents.
FAQ
A benchmark from Sierra that tests agents in multi-turn customer-service conversations, where a language-model-simulated user talks to an agent that has domain API tools and a written policy to follow.
tau-bench sits in the Agents part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Developers Digest publishes tutorials and videos that cover Agents topics including tau-bench. Check the blog and YouTube channel for hands-on walkthroughs.
Related
A multi-agent mechanism where one agent passes control of the conversation to a more specialized agent, rather than calling it as a subroutine.
A pattern where an AI agent uses the output of one tool call as the input for the next, building a multi-step pipeline of actions.
A reliability metric that counts a task as solved only if all k independent attempts succeed, the strict counterpart to pass@k.

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