Enterprises need to tie their generative AI and agentic development efforts to foundational IT principles. AI development frameworks have emerged to address this need.
Over the past few months we have heard quite a bit about AI agents and agentic development. Last September, I, and to be honest, I am not sure we have even seen peak agentic hype yet. So, everyone should expect the term to be stretched, pulled, and completely overdone by springtime. That said, I firmly believe that agentic is a simultaneously powerful and pragmatic way to generate business results from AI technology.
Agentic development is more complex than development for other types of AI apps and requires a mix of capabilities including natural language, visual processing, machine learning, proprietary data, business logic, workflows, security, robotic actions, deterministic programming . . . and the list goes on and on.
While we have seen great progress in the embedding of agentic capabilities in SaaS offerings , those are more traditionally directed at providing agentic capabilities to existing data, workflows and tools — versus the more horizontal approach in an ADF.Ultimately, when an enterprise makes a strategic technology decision, the company will want something that provides as much flexibility as it can reasonably manage with the resources it has — human, financial and technical.
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