
Sign up to save your podcasts
Or


We dive into the latest paper from a team of researchers at IBM: "From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production." We're excited to host several of the paper's authors, who walk us through the research and its implications. The paper reports IBM’s experience developing and piloting the Computer Using Generalist Agent (CUGA), which has been open-sourced for the community. CUGA adopts a hierarchical planner–executor architecture with strong analytical foundations, achieving state-of-the-art performance on AppWorld and WebArena. Beyond benchmarks, it was evaluated in a pilot within the Business-Process-Outsourcing talent acquisition domain, addressing enterprise requirements for scalability, auditability, safety, and governance.
CUGA code: https://github.com/cuga-project/cuga-agent
Paper: https://arxiv.org/abs/2510.23856
Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.
By Arize AI5
1515 ratings
We dive into the latest paper from a team of researchers at IBM: "From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production." We're excited to host several of the paper's authors, who walk us through the research and its implications. The paper reports IBM’s experience developing and piloting the Computer Using Generalist Agent (CUGA), which has been open-sourced for the community. CUGA adopts a hierarchical planner–executor architecture with strong analytical foundations, achieving state-of-the-art performance on AppWorld and WebArena. Beyond benchmarks, it was evaluated in a pilot within the Business-Process-Outsourcing talent acquisition domain, addressing enterprise requirements for scalability, auditability, safety, and governance.
CUGA code: https://github.com/cuga-project/cuga-agent
Paper: https://arxiv.org/abs/2510.23856
Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

32,238 Listeners

109 Listeners

548 Listeners

1,064 Listeners

113,520 Listeners

235 Listeners

82 Listeners

6,113 Listeners

204 Listeners

780 Listeners

10,218 Listeners

101 Listeners

561 Listeners

5,594 Listeners

102 Listeners