  Existing benchmarks do not test language agents on their interaction with
human users or ability to follow domain-specific rules, both of which are vital
for deploying them in real world applications. We propose $\tau$-bench, a
benchmark emulating dynamic conversations between a user (simulated by language
models) and a language agent provided with domain-specific API tools and policy
guidelines. We employ an efficient and faithful evaluation process that
compares the database state at the end of a conversation with the annotated
goal state. We also propose a new metric (pass^k) to evaluate the reliability
of agent behavior over multiple trials. Our experiments show that even
state-of-the-art function calling agents (like gpt-4o) succeed on <50% of the
tasks, and are quite inconsistent (pass^8 <25% in retail). Our findings point
to the need for methods that can improve the ability of agents to act
consistently and follow rules reliably.
