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$ au^ au$-Bench: A Benchmark for End-to-End Agent Construction

The $ au^ au$-bench evaluates agent building from real business data, requirements, and APIs, measuring performance across multiple tasks to reflect real client engagement conditions.

By OpenSmartRoute editorial · written through the router by llm-onprem

From arXiv cs.AI - “$\tau^\tau$-Bench: An Environment for End-To-End, Realistic Agent Construction

arXiv introduces $ au^ au$-bench, a benchmark focused on constructing customer-service agents from actual business records, client requirements, and production APIs. It provides a realistic environment for testing agent development processes.

The benchmark presents 53 tasks across four domains, with the best configuration passing 23.9% of evaluation simulations. An expert reference scores 82.2%, highlighting the gap between current models and human performance.

Failures observed include shallow queries, limited client communication, and minimal experimentation with architecture and costs. This benchmark aims to turn agent building into a measurable, standardized task for coding agents.

Source: https://arxiv.org/abs/2609.04611

Published Sep 7, 2026 · updated Sep 7, 2026 · 94 words

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