The Agentic AI (aka AI Agents) Report

Key Points:

• Agentic AI: Agentic AI systems represent a significant advancement in AI, characterized by their ability to perceive, reason, and act autonomously, extending human capabilities in unprecedented ways.
• Definitions and Perspectives: Various definitions from organizations like OpenAI, IBM, and Google DeepMind highlight the multifaceted nature of agentic AI, emphasizing goals, actions, and natural language interfaces. At Artificiality, we define agentic AI systems as those that can perceive, reason, and act with varying complexity to extend the human mind beyond our current experience.
• Historical Context and Development: The concept of AI agency has evolved from early systems like IBM's Deep Blue to advanced platforms such as AutoGPT and LangChain, showcasing rapid advancements in the field.
• Core Components: Agentic AI systems are built on three main components:
  • Perception: Utilizing advanced sensors and machine learning for environmental understanding.
  • Reasoning: Combining logical reasoning, probabilistic inference, and heuristic decision-making.
  • Action: Executing tasks through pre-defined behaviors, learned skills, and adaptive control.
• Agentic AI Personas: Different combinations of perception, reasoning, and action create varied AI personas, from simple aides to complex wayfinders, each suited for specific applications and levels of complexity.
• Impacts and Considerations: The deployment of agentic AI has the potential to significantly impact daily tasks and strategic decision-making, but also raises concerns about control, ethical considerations, and unintended consequences.
• Designing for AI Agents: The shift from customer experience (CX) to agent experience (AX) requires new design principles to accommodate AI agents, focusing on efficiency, transparency, and the ability to process vast amounts of information.
• Organizational Structures: Single-agent and multi-agent architectures (vertical and horizontal) each have distinct advantages and challenges, influencing scalability, robustness, and flexibility.

PDF version of this report is available at the bottom of this page.

Introduction

Recent developments in AI have given rise to a new class of systems known as agentic AI. These systems are characterized by their ability to perceive, reason, and act with varying levels of complexity, extending human capabilities in unprecedented ways. While there isn’t a single definition of agentic AI—what is agreed upon is that  agentic AI represents a significant leap.

OpenAI describes agentic AI systems as those that "can pursue complex goals with limited direct supervision." Researchers from Stanford, Microsoft, and UCLA characterize AI agents as "a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data, and can produce meaningful actions." IBM views AI agents as "language model-powered entities able to plan and take actions to execute goals  over multiple iterations," while Google DeepMind defines advanced AI assistants as "artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user—across one or more domains—in line with the user's expectations."

At Artificiality, we define agentic AI systems as those that can perceive, reason, and act with varying complexity to extend the human mind beyond our current experience. This definition emphasizes the trio of capabilities—perception, reasoning, and  action—that we will use to frame various capabilities of AI agents later in this report.

The concept of AI agency AI is not entirely new, with roots tracing back to early systems like IBM's Deep Blue chess-playing system in 1997 and the debut of virtual assistants like Siri in 2011. However, recent years have seen a rapid acceleration in the development and deployment of increasingly sophisticated agentic AI systems. Early experiments like AutoGPT and BabyAGI caught the AI community’s attention while newer tools and platforms like Brevian, CrewAI, LangChain, and Qurrent provide the ability for individuals and organizations to develop their own AI agents. 

We are focused on agentic AI because we believe a) it has the potential to have wide-ranging impacts on how we live and work, b) these technologies have the potential to both empower and disempower, and c) some level of agency is the future of AI. Giving machines the agency to perceive, reason, and act to accomplish goals might be quite useful with everyday tasks. But there is also the possibility for AI agents to wreak havoc if left unchecked. Since everyone with a computer now has the ability to create agentic AI, there may be little to no ability to control how and why these agents do what they do. 

In this report, we will explore the key components of agentic AI systems, including perception, reasoning, and action. We'll examine how these capabilities can be combined in various ways to create AI agents with different roles and personas, from simple task executors to complex decision support systems. We'll also consider the realities of deploying agentic AI in real-world contexts, including the shift from designing for human customers to designing for AI agents themselves. Finally, we'll explore the challenges and opportunities of building multi-agent systems and the importance of assembling diverse, multidisciplinary teams to effectively develop and integrate agentic AI technologies.

If you thought that LLMs changed everything (which they did), agentic AI will change everything even more, even if its full instantiation is many years away.