Towards Enterprise-Ready Computer Using Generalist Agent
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916833555120128 |
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| author | Marreed, Sami Oved, Alon Yaeli, Avi Shlomov, Segev Levy, Ido Akrabi, Offer Sela, Aviad Adi, Asaf Mashkif, Nir |
| author_facet | Marreed, Sami Oved, Alon Yaeli, Avi Shlomov, Segev Levy, Ido Akrabi, Offer Sela, Aviad Adi, Asaf Mashkif, Nir |
| contents | This paper presents our ongoing work toward developing an enterprise-ready Computer Using Generalist Agent (CUGA) system. Our research highlights the evolutionary nature of building agentic systems suitable for enterprise environments. By integrating state-of-the-art agentic AI techniques with a systematic approach to iterative evaluation, analysis, and refinement, we have achieved rapid and cost-effective performance gains, notably reaching a new state-of-the-art performance on the WebArena and AppWorld benchmarks. We detail our development roadmap, the methodology and tools that facilitated rapid learning from failures and continuous system refinement, and discuss key lessons learned and future challenges for enterprise adoption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01861 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Enterprise-Ready Computer Using Generalist Agent Marreed, Sami Oved, Alon Yaeli, Avi Shlomov, Segev Levy, Ido Akrabi, Offer Sela, Aviad Adi, Asaf Mashkif, Nir Distributed, Parallel, and Cluster Computing Artificial Intelligence Multiagent Systems This paper presents our ongoing work toward developing an enterprise-ready Computer Using Generalist Agent (CUGA) system. Our research highlights the evolutionary nature of building agentic systems suitable for enterprise environments. By integrating state-of-the-art agentic AI techniques with a systematic approach to iterative evaluation, analysis, and refinement, we have achieved rapid and cost-effective performance gains, notably reaching a new state-of-the-art performance on the WebArena and AppWorld benchmarks. We detail our development roadmap, the methodology and tools that facilitated rapid learning from failures and continuous system refinement, and discuss key lessons learned and future challenges for enterprise adoption. |
| title | Towards Enterprise-Ready Computer Using Generalist Agent |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2503.01861 |