1、LEADERS NOTEGenerative AI opened the floodgates to AI innovation,particularly in real,scalable,enterprise applications of AI with AIagents to,as they say,“make AI do what it was designed to do.”AI agents are software entities that can leverage all the learning,reasoning,context awareness being immer

2、sed intoincreasingly sophisticated large language models,multimodal models,and now large action/reasoning models,along withreal-world interactions,to do what humans do.Today,at a nascent scale,but with the promise of immense autonomy andagency sometime in the future!Agentic AI systems multi-agent sy

3、stems with human+AI collaboration or fully autonomous systems with minimal humaninput will eventually become mainstream enterprise applications.But at the core,AI agents are built on the capabilities ofprevious generations of AI technologies both predictive and generative and in that,these systems a

4、lso inherit inherentchallenges of both.Adoption,scale,and real-world RoI from agentic AI systems will be determined by an enterprisesability to overcome these inherent data,trust,AI risk,and human mindset challenges before its competitors do.Our study,“EnterpriseEnterprise ExperimentsExperiments wit

5、hwith AIAI AgentsAgents 20252025 GlobalGlobal TrendsTrends,”the first of a two-part study set,offers a peakinto the evolution journey of current AI agents and potential models of the future.The study offers a comprehensiveanalysis of the current state of enterprise interest,adoption,preferences,chal

6、lenges,and risk perception associated withdeploying AI agents in the near-mid-term,based on a global survey of over 100 large and medium-sized enterprises.Companies are optimistic about the promise of AI agents.Yet,they prefer piloting internally before taking solutions toclients.Promise is also ref