Agents
A reliability metric that counts a task as solved only if all k independent attempts succeed, the strict counterpart to pass@k.
A reliability metric that counts a task as solved only if all k independent attempts succeed, the strict counterpart to pass@k. It was introduced in the tau-bench paper and is estimated from n trials with c successes as C(c, k) / C(n, k). Because a single failure sinks the score, pass^k drops as k grows for any agent that is only right some of the time: in tau-bench, strong agents solved under 50% of tasks and fell below 25% at pass^8 in retail. It is the better metric when an agent will repeat the same kind of task in production.
Primary source: arxiv.org
Example
In practice, developers reach for Pass-Hat-k (pass^k) 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 reliability metric that counts a task as solved only if all k independent attempts succeed, the strict counterpart to pass@k.
Pass-Hat-k (pass^k) 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 Pass-Hat-k (pass^k). Check the blog and YouTube channel for hands-on walkthroughs.
Related
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 multi-agent mechanism where one agent passes control of the conversation to a more specialized agent, rather than calling it as a subroutine.
A multi-agent pattern where many lightweight agents work on sub-tasks simultaneously without a central orchestrator.

New tutorials, open-source projects, and deep dives on coding agents - delivered weekly.