What are the five principles to keep in mind for AI agent implementation and future integration?

In a survey of executives at large companies, 10% of organizations have already implemented AI agents, and more than half say they plan to do so in the near future.
The technology differs from traditional/generative AI in that it is specialized for specific tasks, operates autonomously, and has decision-making capabilities.

AI agents are attracting attention for a variety of reasons, including streamlining and optimizing decision making, reducing lead time to product release, in addition to automating routine tasks,
innovation, and many other benefits.

As with other AI technologies, the quality of data and learning models directly affects the quality of AI agents’ deliverables.
In addition to data accuracy, ensuring efficiency and governance is also important when deploying AI agents.

Unlike AI/generative AI that uses general-purpose data and learning models LLM based on such data, AI agents directly handle corporate and personal data, which requires particularly strict measures to ensure governance and data storage methods.

In addition to explaining the basics of AI agents, we report that some advanced companies are implementing AI agents on a department-by-department, business-by-business basis, with the expectation that they will be integrated in the future.
We hope that you will request a copy of the report, which also includes five points to keep in mind when implementing, three requirements for data infrastructure, and a recommended data platform.