AI Agents: The Rise of Intelligent Agents in AI Technology

AI agents
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Table of Contents

Artificial intelligence (AI) has profoundly reshaped how humans and machines interact, empowering machines to make decisions and perform tasks to aid humans. At the heart of AI, we encounter entities called intelligent agents (IAs)

These intelligent entities, commonly called AI agents, possess the ability to perceive and analyze their environments, empowering them to take reasoned actions to achieve specified goals.

Developing sophisticated AI systems that operate effectively in dynamic environments featuring diverse AI agents is entirely plausible.

In this article, we will look into the concept of AI agents, explore the various types of agents in AI, and examine their practical applications in real-world scenarios.

What is an AI agent?

In AI, an agent is a computer program or system crafted to perceive its surroundings, make decisions, and take actions to achieve specific goals. Put simply, when we say “agent,” we mean software that can understand natural language and perform diverse tasks based on user knowledge. Operating independently, an agent functions autonomously without direct human control.

Intelligent agents are categorized based on reactivity, proactivity, environmental stability, and the system’s structure. Reactive agents respond promptly to stimuli, while proactive agents plan ahead to achieve goals. Environments can be fixed or dynamic, with fixed rules or constantly changing scenarios.

Multi-agent systems involve multiple agents collaborating towards a shared goal, necessitating coordination and communication. Agents are employed across various domains, such as gaming, robotics, and intelligent systems, making use of a wide array of programming languages and methodologies. This includes applying diverse techniques like machine learning (ML) and natural language processing (NLP).

In the context of artificial intelligence, a rational agent encompasses entities like individuals, firms, machines, or software capable of decision-making. This agent takes actions yielding the optimal outcome after assessing past and current percepts (perceptual inputs at a given instance). The AI system comprises an agent and its environment, with agents perceiving through sensors and acting through actuators.

Video source: YouTube/Simplilearn

AI Agents vs. Bots

To witness the transformative potential of agents, let’s contrast them with existing AI tools, predominantly bots. These bots are confined to specific applications, intervening only when prompted by particular words or queries. Lacking the ability to remember interactions, they don’t evolve or adapt to user preferences, distinguishing them from the concept of agents. 

AI agents exhibit heightened intelligence, proactively making suggestions and seamlessly navigating various applications. Their continuous improvement stems from retaining user history, recognizing intent, and discerning behavioral patterns. While agents propose tailored solutions based on gathered information, users have the final decision-making authority.

Consider planning a trip: a travel bot identifies budget-friendly hotels, while an AI agent, aware of your travel habits, suggests destinations and recommends activities based on your interests. The real breakthrough is the democratization of services. AI agents will significantly impact healthcare, productivity, education, shopping, and entertainment, making previously expensive services accessible to a broader audience.

How AI Agents Work

When you assign a task to an AI agent, it begins by understanding your goal. It forwards the prompt to the core large language model (LLM), such as GPT-3.5 or GPT-4, generating its initial output to demonstrate comprehension.

The subsequent phase involves constructing a task list. Aligned with the objective, it generates tasks and determines their sequential order. Once it establishes a viable plan, the agent starts scouring for information.

Leveraging computer capabilities akin to human users, the agent navigates the internet for data. Some agents even collaborate with other AI models to delegate tasks, accessing features like image generation, computer vision, or geographical data processing.

The agent autonomously manages and stores all data, facilitating user communication and refining its strategy. 

Upon completing tasks, it gauges its proximity to the goal through feedback sourced externally and from its internal thought process. It continually iterates until the objective is achieved, generating new tasks, collecting information and feedback, and progressing without interruption.

These steps outline the fundamental process of a conventional AI agent in achieving diverse goals. Developers may organize these steps differently based on their agent configurations. 

What are the 5 Types of AI Agents?

To build effective AI systems, a thorough grasp of various AI entities is crucial. Each AI agent type tackles distinct challenges, providing nuanced solutions and adaptability. These agents emulate human behavior by acting intelligently and rationally. 

Their proficiency lies in adeptly perceiving and analyzing sensor information, empowering them to take necessary actions for optimal performance. 

Let’s explore the five types of AI entities:

1. Simple Reflex AI Agents

Simple reflex agents make decisions based solely on current information, ignoring past perceptions. Relying on condition-action rules, they link specific states to actions. Yet, in partially observable environments, these agents often face infinite loops. 

Their drawbacks include limited intelligence, a lack of awareness beyond immediate perception, size challenges in rule management, and the need for constant updates when environmental changes occur, complicating their operation.

2. Model-Based Reflex AI Agents

Model-Based Reflex Agents operate by identifying rules aligning with the current scenario. In dealing with partially observable environments, these agents employ a world model, tracking internal states adjusted with each percept and influenced by percept history. 

Storing the current state involves maintaining a structure representing the unseen part of the world. To update this state effectively, the agent needs insights into how the world evolves independently and the impact of its actions on the environment.

3. Goal-Based AI Agents

Goal-based agents decide actions based on their proximity to the desired outcome. Each move is aimed at minimizing the distance to the goal, enabling the agent to navigate diverse options and choose the path leading to a goal state. 

These agents possess explicit, modifiable knowledge, enhancing flexibility. Typically involving search and planning, their adaptable behavior allows for easy adjustments, making them dynamic problem-solvers capable of addressing evolving scenarios.

4. Utility-Based AI Agents

Utility-based agents, foundational for efficient decision-making, assess and choose optimal actions among alternatives based on state preferences (utility). They consider factors like safety, speed, and cost for destination selection. Agent happiness, a crucial metric, is quantified by the utility function. 

When navigating uncertainties, these agents optimize actions to maximize expected happiness, using the utility function to assign numerical values to each state’s happiness level. This ensures a systematic approach for decisions aligned with agents’ overall well-being and satisfaction.

5. Learning AI Agents

These entities exhibit a remarkable capacity for learning novel approaches to enhance their performance, leveraging experiences gained over time. The process involves assimilating percepts into their internal state, thereby laying the groundwork for more informed decision-making in the future. 

The key constituents of a learning agent revolve around four main conceptual elements:

  • Learning element: This component is tasked with effecting enhancements by assimilating knowledge from its surroundings.
  • Critic: The learning element solicits feedback from critics, offering assessments of the agent’s performance relative to established benchmarks.
  • Performance element: This segment assumes responsibility for selecting external actions based on acquired knowledge.
  • Problem generator: This element suggests actions that will open avenues for novel and enlightening experiences.

Where are AI Agents Used?

Examples of AI agents in action showcase their diverse applications across various domains:

  • Virtual societies: In a simulated town experiment inspired by Sims, orchestrated by Stanford University and Google, 25 AI agents engaged in interactions, exchanging information, establishing relationships, and coordinating events, including a Valentine’s Day party. This showcased the capacity of AI agents to replicate and simulate social dynamics and behaviors within a virtual environment.
  • Self-driving cars: AI agents play a crucial role in the operation of self-driving cars. These agents navigate the vehicle from one point to another, ensuring adherence to traffic rules and cooperation with other vehicles. The future may see enhanced coordination among self-driving systems, fostering a multi-agent AI approach.
  • Computational assistance: AI agents on personal computers are adept at tasks such as research. They can scour the internet for information, organize data, generate summaries, and present results. The convenience lies in the ability to delegate these tasks to AI agents, allowing humans to focus on higher-level activities.
  • Collaborative human-AI teams: Envisioning the future, smaller human teams could leverage large AI agent teams to enhance organizational efficiency. While humans handle strategic aspects and interpersonal relationships, AI agents take on automation tasks. This collaborative approach may extend to personal AI agents interacting with counterparts from different companies and government bodies.
  • General applications: AI agents find utility in a wide array of fields, including robotics, smart homes, transportation systems, healthcare, finance, gaming, NLP, cybersecurity, environmental monitoring, and social media analysis. Their versatility enables them to tackle diverse challenges and contribute to problem-solving across various industries.

As AI models evolve, these agents may grasp more nuanced tasks, expanding their capabilities and applicability. With the use of LLM reasoning, the potential for addressing complex objectives in the future becomes increasingly promising. The key lies in continually improving these models to enhance AI agents’ understanding and problem-solving capabilities.

The Future Goals of AI Agents

Performing tasks on a computer currently involves navigating various apps, each with limited insights into your life. While useful for certain functions, Google Docs and LibreOffice fail to understand and assist with broader activities. However, a paradigm shift is imminent. 

Over the next five years, the need for diverse apps will fade, replaced by a more straightforward approach. You’ll communicate your tasks in everyday language, and the software, leveraging AI, will respond with a deep understanding of your life.

Despite past attempts with digital assistants, future AI agents promise superior capabilities. They facilitate nuanced conversations and handle a broad array of tasks, going beyond simple functions like letter writing. 

What makes intelligent agents appealing is their all-encompassing assistance. With permission to track online interactions and real-world activities, they gain profound insights into your life, encompassing personal and professional aspects. Users maintain control, deciding when and how the agent intervenes.

As exemplified by the groundbreaking NExT-GPT, the first end-to-end general-purpose any-to-any Multimodal Large Language Model (MM-LLM), AI agents are reshaping software creation, eliminating the need for coding skills. This revolutionary model seamlessly processes diverse inputs – text, image, video, audio – generating outputs across modalities. 

This points towards a future where non-developers effortlessly craft personal assistants, fundamentally altering software use and development. These agents offer efficient alternatives to search engines, e-commerce, and productivity apps, shaping a dynamic and cost-effective environment.

Video source: YouTube/AI Bites

AI Agents: Key Takeaways

Artificial intelligence is experiencing a groundbreaking shift driven by intelligent agents (IAs). Exploring AI agents’ types and applications, from simulated town experiments to self-driving cars, reveals their profound impact on diverse sectors. 

Categorized by goals and learning capacities, these agents promise a future where communication with software is as intuitive as everyday conversation. The NExT-GPT model exemplifies this evolution, empowering non-developers to effortlessly create personal assistants. 

As we anticipate the next wave of AI advancements, the collaborative synergy between humans and AI agents will undoubtedly redefine how we navigate technology, making tasks more intuitive, personalized, and efficient. 

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.