What Is an AI Agent? The Comprehensive Guide 2024

An AI agent is a computational entity designed to perform specific tasks or solve problems autonomously using artificial intelligence techniques. While it does not yet reach the science fiction of the AI personal assistant of Tony Stark’s Jarvis, it can perceive their environment, process information, make decisions, and take actions to achieve predefined objectives with its large-language models (LLM).

Demo of Google’s AI agent.

This is insane 🤯 pic.twitter.com/EJWIgBkwpF

— Alvaro Cintas (@dr_cintas) May 14, 2024

According to Sergio Gago, managing director of AI and quantum computing at financial services firm Moody’s, “An AI agent is a unique entity that has agency. Instead of telling them what to do or asking them questions to get an answer, you instruct a business goal or specify goals and allow them to do the planning.”

For example, unlike AI chatbots like ChatGPT, an AI agent will continue running once you give them an objective or a stimulus to trigger their behaviour without needing additional prompts and instructions. In short, an AI agent continuously assesses its surroundings, learns from interactions and makes choices to achieve specific objectives by leveraging its adaptability and learning capabilities.

AI Agent vs AI Chatbot

While both AI agents and AI chatbots utilise artificial intelligence, their roles, capabilities, and applications differ significantly. Attached is a table explaining their differences:

AI Agent	Differences	AI Chatbot
Uses AI to perceive its environment, make decisions, and take actions to achieve specific goals	Purposes	Uses natural language processing (NLP) to understand and respond to text or voice inputs
Operates independently without human intervention	Autonomy	Operates within the scope of its designed tasks and capabilities
Operate autonomously in diverse environments, and interact with the physical world or digital systems	Functionality	Understanding and generating natural language to interact with users
Autonomous vehicles (Self-driving cars), Robotic systems (industrial robots), Intelligent assistants (Smart Home Systems)	Examples	Customer service bots, Virtual assistants (Siri, Google Assistant), Informational bots
Industrial automation, Healthcare, Finance, Transportation	Applications	Customer service and support, E-commerce, Education

How Does An AI Agent Function?

An AI agent functions through a sophisticated perception, decision-making, action, and learning cycle. In the words of Rudina Seseri, founder and managing partner at Glasswing Ventures, “An AI Agent incorporates various AI/ML techniques such as natural language processing, machine learning, and computer vision to operate in dynamic domains, autonomously or alongside other agents and human users.”

Listed below is a breakdown of how an AI Agent functions:

1. Perception Through Sensors and Data Collection: After receiving instructions, the AI agent will use sensors to gather data from their environment, such as visual data from cameras, auditory data from microphones, or any other sensory input relevant to the task.
2. Data Processing: The data collected in the first step is raw, thus requiring the agent to process the raw data to extract meaningful information, like image recognition, natural language processing, or other forms of data analysis. The agent will then store the data in its learning system, allowing it to improve and refine its strategy and adapt to new scenarios.
3. Decision Making: After understanding its current situation and the context for its actions, the AI agent will generate ideas for tackling the task at hand, selecting the best possible solution based on its search algorithms, decision trees, or more complex methods like reinforcement learning.
4. Carrying Out Tasks Via Actuators: Actuators are components that allow the agent to interact with its environment and take action. For instance, it could be physical mechanisms like robotic arms and wheels or digital processes like text generation or generating natural language, recognising emotions, and adhering to social norms.
5. Feedback Learning and Adaptation: Once it completes its current tasks, the AI agent will assess the success or failure of the actions taken and use feedback to improve their decision-making processes over time by updating its internal models and strategies based on the new data and experiences.

Types of AI Agents

An AI agent can be classified based on its complexity and capabilities. Listed are the five primary types of AI agents:

• Simple Reflex Agents

To summarise, simple reflex agents function on the condition-action rules, meaning they act solely based on the current percept, ignoring the rest of the percept history. For example, a thermostat that adjusts temperature based on the current reading. They are straightforward and efficient but limit their effectiveness in complex, unstructured environments.

• Model-Based Reflex Agents

Unlike simple reflex agents who do not keep track of the past, model-based reflex agents build and maintain their own perception of the world, gathering information about their environment and how their actions affect it, allowing them to handle more complex environments and tasks. For instance, a robotic vacuum that maps out a room and remembers obstacles to optimise cleaning routes.

• Goal-Based Agents

Following up on model-based, goal-based agents go a step further by considering the future consequences of their actions. For example, they create a strategy to solve a particular problem, such as generating a task list, taking steps to solve it, and understanding whether those actions are moving them closer to the goal. For example, navigation systems that find the shortest route to a destination

• Utility-Based Agents

To go beyond, utility-based agents not only aim to achieve goals but also consider the utility or value of different outcomes as they strive to maximise their overall performance based on a utility function. For example, a financial trading algorithm and flight selectors run each possibility and score it based on its utility function, complex reasoning methods to compare different scenarios and their respective pros and cons, and then choose the option that provides users with the highest overall benefit based on their preferences.

• Learning Agents

As the name implies, these agents improve their performance gradually by learning from their experiences. They use various learning techniques, such as reinforcement learning and even have a sort of internal criticism to evaluate past results, comparing the actions they’ve taken to the effect they’ve had on their environment. You can see this agent in action in most recommendation systems.

Examples and Applications of Existing AI Agents

An AI agent is integral to modern technology and continues to push the boundaries of what machines can achieve as it revolutionises various sectors by automating tasks, enhancing efficiency, and providing intelligent solutions. From virtual assistants to autonomous vehicles, here are some of the best examples of existing AI agents:

• Virtual Assistants (Siri, Alexa, Google Assistant)
• Autonomous Vehicles (Waymo, Tesla Autopilot)
• Healthcare (Ada Health)
• Financial Trading Algorithms (BlackRock’s Aladdin)
• Gaming AI (DeepMind’s AlphaGo)
• Industrial Robots (Boston Dynamics’ Spot)
• Content Recommendation Systems (Netflix and Spotify)
• Smart Home Devices (Roomba iRobot)
• Security Surveillance Systems (BriefCam)

Also Read: Thrive AI Health: The New AI Health Coach Funded by Arianna Huffington and Sam Altman

Parting Thoughts

With everything considered, as AI technology advances, the capabilities and impact of AI agents are expected to grow, leading to even more sophisticated and beneficial applications. However, we are still far off from a fully autonomous AI agent.

According to David Cushman, a research leader at HFS Research, “I think it’s the next step. It’s where AI is operating independently and effectively at scale. So this is where humans set the guidelines and guardrails and apply multiple technologies to take the human out of the loop — when everything has been about keeping the human in the loop with GenAI.”

As for AI agents potentially replacing human jobs, Sergio Gago stated: “The people who use agents become super-powered. It’s not so much about replacing people, but more about superhumanising them.”

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