How can we operate faster and more efficiently?

This question has always been at the forefront of strategic agendas—but Generative AI (GenAI) is helping unlock new answers. Now the question for business and government leaders is becoming:

How can we rethink our business processes with GenAI?

Large language models (LLMs) and GenAI-powered tools used by most organizations today serve as helpful assistants. What if GenAI could be more like a skilled collaborator that will not only respond to requests but also plan the whole process to help solve a complex need?

This vision is becoming a reality with the emergence of AI agents and multiagent AI systems—a powerful advancement in what’s possible through human-AI partnership. Leading companies and government agencies are already seeing the value of AI agents and putting them into practice.

AI agents are reasoning engines that can understand context, plan workflows, connect to external tools and data, and execute actions to achieve a defined goal. While this may sound broadly like what standalone LLMs or GenAI applications can do, there are key distinctions that make AI agents significantly more powerful.

Typical LLM-powered chatbots, for example, usually have limited ability to understand multistep prompts—much less to plan and execute whole workflows from a single prompt. They also struggle to reason over sequences, such as compositional tasks that require consideration of temporal and textual contexts.

AI agents excel in addressing these limitations while also leveraging capabilities of domain- and task-specific digital tools to complete more complicated tasks effectively.

While individual AI agents can offer valuable enhancements, the truly transformative power of AI agents comes when they work together with other agents.

Multiagent AI systems employ multiple, role-specific AI agents to understand requests, plan workflows, coordinate role-specific agents, streamline actions, collaborate with humans and validate outputs.

Multiagent AI systems typically involve standard-task agents (e.g., user interface and data management agents) working with specialized-skill and -tool agents (e.g., data extractor or image interpreter agents) to achieve a goal specified by a user.

At the core of every AI agent is a language model that provides a semantic understanding of language and context—but depending on the use case, the same or different language models may be used by agents in a system. This approach can allow some agents to share knowledge while others validate outputs across the system—improving quality and consistency in the process. That potential is further enhanced by providing agents with shared short- and long-term memory resources that reduce the need for human prompting in the planning, validation and iteration stages of a given project or use case.