Τ³-Bench is out – can agents handle complex docs and live calls?
τ-Bench is an open benchmark for evaluating AI agents on grounded, multi-turn customer service tasks with verifiable outcomes. It's been great to see the community adopt it since launch — this is now the third iteration. With τ³-Bench, we're extending it to two new settings: knowledge-intensive retrieval and full-duplex voice. τ-Knowledge: agents must navigate ~700 interconnected policy documents to complete multi-step tasks. Best frontier model (GPT-5.2, high reasoning) hits ~25%. The surprising part: even when you hand the model the exact documents it needs, performance only reaches ~40%.…
In plain words
τ³-Bench is an open benchmark for evaluating AI agents on customer service tasks with verifiable outcomes. The third iteration extends testing to two new domains: knowledge-intensive retrieval, where agents navigate approximately 700 interconnected policy documents to complete multi-step tasks, and full-duplex voice interactions with realistic audio conditions like accents and background noise. It targets researchers and developers building AI agent systems, revealing that reasoning over complex, interlinked information and action sequencing—rather than document retrieval—represents the primary performance bottleneck.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
τ-Bench is an open benchmark for evaluating AI agents on grounded, multi-turn customer service tasks with verifiable outcomes. It's been great to see the community adopt it since launch — this is now the third iteration. With τ³-Bench, we're extending it to two new settings: knowledge-intensive retrieval and full-duplex voice. τ-Knowledge: agents must navigate ~700 interconnected policy documents to complete multi-step tasks. Best frontier model (GPT-5.2, high reasoning) hits ~25%. The surprising part: even when you hand the model the exact documents it needs, performance only reaches ~40%. We found that the bottleneck isn't retrieval — it's reasoning over complex, interlinked policies and executing the right actions in the right order. τ-Voice: same grounded tasks, but over live full-duplex voice with realistic audio — accents, background noise, interruptions, compressed phone lines. Voice agents score 31–51% in clean audio conditions and 26–38% in realistic ones. A consistent failure pattern across providers (OpenAI, Gemini, xAI): agent mishears a name or email during authentication, and everything downstream fails. We also incorporated 75+ task fixes to the original airline, retail, and telecom domains — many based on community audits and PRs (including contributions from Amazon and Anthropic). We believe a benchmark is only as good as its maintenance, and we're grateful for the community's help improving it. Code and leaderboard are open — we'd welcome community submissions and feedback. Blog post (papers, code, leaderboard): https://sierra.ai/blog/bench-advancing-agent-benchmarking-to...
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