AI agents are capable of superior, more sophisticated performance than traditional, one-dimensional AI assistants, particularly their ability to manage and execute complex tasks. This is because an AI agents can engage with multiple assets, retain knowledge and experience. Notably, the ability of AI agents to collaborate is unique feature that contributes to performance gains over one-dimensional chat assistants, for example by collaborating to synthesize data and perform interdependent tasks. Moreover, an AI agent can apply skills it has used in one context and apply them to other processes. Taken together these capabilities mean that AI agents can provide responses and handle tasks in ways that are more adaptable, accurate, contextually aware and personalised, which means a better user experience. This is particularly important in sales and marketing, and customer support use cases where customer expectations are high. A pleasing user experience is also crucial for enterprise employees using AI agents, as this can increase work satisfaction.

Agents can process and analyse vast amounts of data, often in near-real time, providing actionable insights on the fly. Real-time, data-driven insights help enterprises make informed decisions quickly, empowering them to be more agile. Enterprises in this position are better placed to capitalise opportunities or mitigate risks as they arise. For example, in the financial services context AI agents could analyse real-time market data, predict trends, and make investment recommendations more efficiently than non-agentic systems, allowing enterprises to react to market changes faster than human analysts. In the procurement domain, AI agents could provide an efficient, fast means of vetting potential suppliers, collating and analysing data relating to a large number of suppliers.