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IBM clients evaluate AI agent capabilities amid cautious adoption

May 7, 2025•Esther Shittu•Original Link•1 minutes
AIagents
IBM
EnterpriseTech

Businesses are cautiously exploring AI agent technology, with some finding it promising but immature while others gradually implement solutions.

BOSTON -- As 'agentic AI' emerges as this year's tech buzzword, IBM and other vendors are promoting tools for creating interconnected AI agents. At its Think 2025 conference, IBM unveiled new features in its Watsonx orchestrate platform that enable rapid agent development and third-party integration.

Mixed Enterprise Reactions

  • Skepticism about maturity: Armando Castro of Mexican apparel company Grupo dportenis expressed concerns about the technology's immaturity, noting inconsistent results across companies despite IBM's promises.
  • Cautious adoption: USAA insurance is taking a measured approach, using IBM's platform for low-risk data management automation while managing potential risks carefully.

Vendor Perspectives

IBM CEO Arvind Krishna emphasized: "Success will be defined by integration and business outcomes. Agents will redefine application development."

Edward Calvesbert, IBM's VP of Watsonx product management, stressed that "agents need better tools, especially data" for effective orchestration.

Implementation Examples

  • USAA uses orchestration agents to automate data requests with built-in risk assessment
  • Early adopters like Grupo dportenis report some success with IBM Watson Studio

Esther Shittu Esther Shittu covers AI systems for Informa TechTarget

Related Coverage

  • Google's Agent2Agent protocol
  • IBM's data strategy

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About the Author

Dr. Sarah Chen

Dr. Sarah Chen

AI Research Expert

A seasoned AI expert with 15 years of research experience, formerly worked at Stanford AI Lab for 8 years, specializing in machine learning and natural language processing. Currently serves as technical advisor for multiple AI companies and regularly contributes AI technology analysis articles to authoritative media like MIT Technology Review.

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Machine Learning
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Deep Learning
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Experience
15 years
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