Autonomous task completion and problemsolving. By integrating various tools, agents can execute tasks — such as application programming interface (API) calls, data entry, emails and calculations — that require constant monitoring or rapid-fire decision-making with minimal human intervention. When AI agents use machine learning (ML) capabilities with goaloriented behavior, they can take on complex challenges in new and efficient ways. Agents can perform tasks in a stepwise approach to follow a logical chain (train of thought) that improves the precision of a response. This makes it easier for organizations to engage with and guide the actions these systems take.

solutions, agents rely heavily on access to high-quality data that can influence the action or task they need to complete. If your enterprise data isn’t in order, now is the time to make that happen. Organizations have been struggling with this challenge for many years. Can we realistically resolve it? We would argue that enterprise data will never be perfect. Trying to make it so could significantly derail your progress in deploying GenAI and realizing tangible value from it. Assemble an open-minded team who can identify the specific challenges that come with managing LLMs. This will enable you to assign the appropriate resources, either internal or external, to address any issues that arise. Also, create a tiger team who tracks and evaluates new tools, datasets and frameworks that may help. It’s a new area, but it’s evolving rapidly. Staying on top of new options is likely to pay off.