Autonomous Agents

And if we talk about autonomous agents, their context ... and why they are the next wave of automations ... ayyyyy of those now "intelligent" macros.

1. Review of the concept of the autonomous agent

The concept of the autonomous agent has evolved significantly in the field of artificial intelligence, becoming a central element in the development of intelligent systems capable of operating independently. At its core, an autonomous agent is a computational entity designed to perceive its environment, make decisions and act without direct human intervention (Franklin & Graesser, 1997). With the advent of Large Language Models (LLMs) in generative AI, the concept of autonomous agent has taken on new dimensions, allowing the creation of agents with more advanced linguistic and reasoning capabilities.

Autonomy, the defining characteristic of these agents, implies the ability to operate independently, adapt to changes in the environment and pursue predefined or self-generated goals. This capability distinguishes them from conventional programs, which typically follow predetermined instructions without the flexibility to adjust to changing conditions (Wooldridge & Jennings, 1995). LLMs have enhanced this autonomy, allowing agents to understand and generate natural language in a more sophisticated way, which significantly expands their ability to interact with complex and dynamic environments (Wooldridge & Jennings, 1995).

2. Comparative Analysis of Autonomous Agent Definitions

In examining the definitions proposed by various authors, one can identify common elements and divergences that enrich our understanding of the concept:

Brustoloni (1991) offers a concise definition, emphasising autonomous and purposeful action in the real world. This definition, although simple, captures the essence of autonomy and goal orientation. Maes (1995) expands on this notion, describing autonomous agents as computational systems that inhabit complex and dynamic environments, capable of sensing and acting autonomously to achieve specific goals or tasks. This definition introduces the importance of the environment and the agent's relationship to it.Franklin and Graesser (1997) go even deeper, characterising the autonomous agent as a system that not only senses and acts in its environment, but does so over time, pursuing its own agenda and affecting what it will sense in the future. Wooldridge and Jennings (1995) emphasise the agent's ability to perform autonomous actions in its environment to fulfil delegated goals. This perspective introduces the notion of externally assigned goals, in contrast to the ‘own agenda’ mentioned by Franklin and Graesser.Russell and Norvig (2016) offer a more generalised definition, describing the agent as any entity that perceives through sensors and acts through actuators. This definition extends the concept to include a wider range of entities, from simple thermostats to complex AI systems.In the context of LLMs and generative AI, we could add a contemporary definition: ‘An autonomous LLM-based agent is a computational system that uses large-scale language models to perceive, reason and act on its environment, generating contextual and adaptive responses based on vast prior knowledge and the ability to process natural language in a sophisticated way’.

3. Conclusions

Analysis of these definitions reveals an evolution in the conceptualisation of autonomous agents from simple reactive systems to complex entities capable of sophisticated learning, adaptation and decision-making. Common elements that emerge include autonomy, perception of the environment, capacity for action, and orientation towards specific goals or tasks. The diversity in definitions reflects the complexity and multidimensionality of the concept of autonomous agents. While some definitions focus on autonomy and agency, others emphasise the relationship to the environment or the pursuit of goals. This variety underlines the need to consider multiple perspectives when designing and implementing autonomous agents in real-world applications.With the integration of LLMs in the field of autonomous agents, new possibilities have opened up for the creation of more sophisticated and versatile systems. These agents can now understand and generate natural language more effectively, allowing them to interact more naturally with humans and other systems, as well as to process and generate textual information in a more advanced way.

In conclusion, the concept of the autonomous agent continues to evolve as artificial intelligence technology advances. The integration of capabilities such as deep learning, symbolic reasoning and long-term planning, together with the advanced linguistic capabilities provided by LLMs, promises to further expand the frontiers of what these agents can achieve, posing new challenges and opportunities for research and development in this field.

References

Franklin, S., & Graesser, A. (1997). Is it an agent, or just a program? A taxonomy for autonomous agents. In International Workshop on Agent Theories, Architectures, and Languages (pp. 21-35).

Springer, Berlin, Heidelberg.Maes, P. (1995). Artificial life meets entertainment: Lifelike autonomous agents. Communications of the ACM, 38(11), 108-114.

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson Education Limited.Wooldridge, M., & Jennings, N. R. (1995).

Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2), 115-152.