Enterprises are gradually embracing AI agents, moving away from traditional cloud-hosted models towards smaller, more distributed implementations that align with established IT practices. A survey of 394 enterprises revealed that 47 of them boast significant AI agent deployments, allowing for deeper insights into the implications and effectiveness of these agents.
One major takeaway is that resource pools alone are insufficient for hosting AI agents. Effective hosting needs a strategic approach that considers the proximity of data resources, applications, and end-users. This situation complicates the clustering of AI agents in a single location, as the optimal hosting solution varies by use case and agent requirements. Moreover, the evolving nature of an AI agent’s database utilization—shaped by changes in its mission and the discovery of new information resources—necessitates flexibility in data sourcing, further complicating hosting considerations.
A critical challenge for these agents lies in managing implicit versus explicit data usage. Unlike conventional software components that have defined data inputs, AI agents often operate without clear data identification, relying instead on the training data within models. This could lead to a situation where additional data resources are identified post-implementation, which in turn may not be centrally located.
As enterprises gain experience with AI agents, they recognize the need for enhancements in data center networks. This ensures robust connectivity between agents and the databases they operate with. Additionally, as AI agents evolve for real-time applications, their hosting must accommodate connections to vital systems like factories or warehouses, optimizing for latency while maintaining availability across a broader network landscape.
From an operational perspective, the underlying message is that enterprises should plan their AI agent integrations meticulously. Flexibility is essential in designing AI agent hosting and network connectivity, especially regarding the potential evolution of agent capabilities and data requirements. The aim is to establish configurations that can adapt rather than relying on fixes that may be costly and challenging to implement later.
Ultimately, planning is the cornerstone of successful AI agent deployment. Unlike traditional software that is built to perform specific functions, AI agents are designed to learn and evolve, thus necessitating a flexible infrastructure to anticipate their growth. Neglecting this can lead to increased operational costs and diminished performance outcomes.
