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Prompting for action: How AI agents are reshaping the future of work

Expanded use cases and impact from GenAI


Key insights from this report

  • AI agents are reshaping industries by expanding the potential applications of Generative AI (GenAI) and typical language models.
  • Multiagent AI systems can significantly enhance the quality of outputs and complexity of work performed by single AI agents.
  • Forward-thinking businesses and governments are already implementing AI agents and multiagent AI systems across a range of use cases.
  • Executive leaders should make moves now to prepare for and embrace this next era of intelligent organisational transformation.
The next era of business process transformation is here
 

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 organisations 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


What makes AI agents different—and why they matter
 

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.

A new paradigm for human-machine collaboration


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

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