How AI agents are reshaping the future of work

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.

At the end of 2023, nearly 1 in 6 surveyed business leaders said GenAI had already transformed their businesses.¹

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.

AI agents don’t just interact. They more effectively reason and act on behalf of the user.

Through their ability to reason, plan, remember and act, AI agents address key limitations of typical language models.

Use case scope

Typical language models: Automate tasks 

AI agents: Automate entire workflows / processes 

Planning

Typical language models: Are not capable of planning or orchestrating workflows 

AI agents: Create and execute multistep plans to achieve a user's goal, adjusting actions based on real-time feedback

Memory & fine-tuning

Typical language models: Do not retain memory and have limited fine-tuning capabilities 

AI agents: Utilize short-term and long-term memory to learn from previous user interactions and provide personalized responses; Memory may be shared across multiple agents in a system 

Tool integration

Typical language models: Are not inherently designed to integrate with external tools or systems 

AI agents: Augment inherent language model capabilities with APIs and tools (e.g., data extractors, image selectors, search APIs) to perform tasks 

Data integration

Typical language models: Rely on static knowledge with fixed training cutoff dates 

AI agents: Adjust dynamically to new information and real-time knowledge sources 

Accuracy

Typical language models: Typically lack self-assessment capabilities and are limited to probabilistic reasoning based on training data 

AI agents: Can leverage task-specific capabilities, knowledge and memory to validate and improve their own outputs and those of other agents in a system

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.

Multiagent AI systems don’t just reason and act on behalf of the user. They can orchestrate complex workflows in a matter of minutes.

• Capability—AI agents can automate interactions with multiple tools to perform tasks.
• Productivity—AI agents can plan and collaborate to execute complex workflows based on a single prompt.
• Self-learning—AI agents can rapidly improve their output quality over time.
• Adaptability—AI agents can reason and plan new approaches, rapidly reference new and real-time data, and engage with other agents to meet changing needs.
• Accuracy—“Validator” agents can interact with “creator” agents to test and improve quality and reliability.
• Intelligence—When agents specializing in specific tasks work together, new levels of machine-powered intelligence are made possible.
• Transparency—Multiagent AI systems enhance the ability to explain AI outputs by showcasing how agents communicate and reason together.