What Are AI Agents?

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In today's AI-obsessed world, businesses are always on the lookout for ways to automate their processes, increase efficiency, and stay ahead of the curve. AI agents, also known as intelligent agents, or “AI Assistants” at turian, are one of the latest advancements in artificial intelligence that can help businesses achieve these goals. These agents can perform various tasks, make decisions, and interact with users in a human-like manner. With each interaction, certain types of agents can learn and improve their performance.

Unlike human agents, who are limited by their physical and mental abilities, AI agents have the potential to work non-stop, analyze vast amounts of data at lightning speed, and make accurate decisions based on that data. It is clear that, AI agents are the future of artificial intelligence and automation in enterprises.

But what exactly are they? How do they differ from AI chatbots or other AI applications? And how can they benefit your business? If these questions are swirling in your mind, then keep reading to find out everything you need to know about AI agents. From their core attributes and components to their various types and future possibilities, we'll cover it all so you better understand the potential of AI agents.

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What is an AI Agent?

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Image credit: Freepik

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An artificial intelligence (AI) agent is a software program that can perform tasks on behalf of a user, often with a level of autonomy and intelligence that is similar to or surpasses that of a human. These agents can automate processes, interact with their environment, and make decisions based on the collected data to perform self-determined tasks to meet predetermined objectives.

Similar to  humans, AI agents independently determine the best course of action to take to achieve a specific goal. They use complex algorithms based on fields such as machine learning, natural language processing, neural networks, or transformer models to process huge amounts of data, learn from it, and make optimal decisions based on their objective function. Some agentic AI systems can even improve their performance over time by learning from their mistakes and by incorporating user (human) feedback. 

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Core Attributes of AI Agents

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Let's dive deeper into the core attributes that make up an AI agent.

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Autonomy

AI agents work autonomously. They make decisions and take actions independently, without any human intervention. This autonomy allows them to perform complex tasks and make real-time decisions on how to best complete a task without constantly relying on human input.

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Continuous Learning

One of the defining characteristics of AI agents is their ability to learn and improve over time from the feedback they receive. This feedback comes from two key sources: a critic or the environment itself. The critic can be a human operator who evaluates the agent's performance or another AI system that provides feedback. Meanwhile, the environment provides feedback in the form of outcomes that result from the agent's actions. This feedback loop allows AI agents to adapt to changing environments, learn from their experiences, and make better decisions in the future. The more data it receives, the better it becomes at completing tasks and meeting goals.

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Reactive and Proactive

AI agents can operate both reactively and proactively. In reactive mode, they perceive and respond to changes in their environment. This allows them to make real-time decisions based on current conditions. On the other hand, in proactive mode, AI agents take initiative and perform tasks toward their objectives. This means they can anticipate potential changes and take action to prevent or mitigate them.

For example, in a customer service scenario, an AI agent can operate reactively by analyzing customer inquiries and providing automated responses. However, it can also operate proactively by identifying potential issues or complaints and alerting a human representative to intervene before they escalate.

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Key Components of an AI Agent

An AI agent might seem like a labyrinth of complexity at first look, but understanding their core components can shine a light on what makes them tick. Here are some of the key components of an AI agent:

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Agent Function

The agent function is the heart and soul of an AI agent. It dictates how the agent transforms data into actions. In simpler terms, it tells the AI what to do based on the information it has collected. It's essentially the "intelligence" of the AI, as it involves reasoning and decision-making to achieve its goals.

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Knowledge Base

This is where an AI agent stores its accumulated knowledge and information about the world and domain it operates in. This knowledge is often predefined or learned through training with data. It acts as the cornerstone of the agent's decision-making process.

For example, a chatbot designed to assist with sales tasks may have a knowledge base that includes information about products, pricing, promotions, internal company policies (e.g. communication style, language, return policies…), and customer preferences. Any business planning to deploy an AI agent needs to train it on or instruct it with specific company data. While large language models (LLMs) can learn from a vast range of data, AI agents designed for specific functions require dedicated training to create outputs that align with the user's journey.

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Percepts

Percepts are the sensory signals that AI agents gather from their environment. These cues provide essential insights into the current state of the observable world in which the agent is operating. For instance, if the AI agent is a customer service chatbot, percepts can include:

• User input (e.g. text)
• Previous customer interactions
• Customer information (e.g. name, account details)
• Context (e.g. time, date, location)
• Language preferences

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Actuator

Actuators are like the muscles of an AI agent that allow it to physically interact with its environment. They can range from simple actions like sending an email to more complex ones like controlling a self-driving car. Simply put, they carry out the decisions made by the agent function. Examples of actuators include:

• Text response generators used by chatbots can generate and send text-based responses to users.
• Service integration APIs that allow chatbots to interact with external systems (like ERP/CRM) to retrieve or update information like customer data, support tickets, or order status as required.
• They can also send notifications and alerts (e.g. email) to keep users informed and engaged with relevant updates.

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Types of AI Agents

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AI agents can be classified into different types based on their capabilities and functions and what they can do. Here are some of the most common types of AI agents:

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1. Simple Reflex Agents

Simple reflex agents are the most basic type of AI agents. They operate based on a set of predefined rules and only consider the current percept (input data). They do not have any memory or ability to learn and can only handle simple tasks. These agents are suitable for tasks with limited complexity and a narrow range of capabilities. They are suitable for straightforward tasks where the environment is fully observable, and no learning or adaptation is required. An example could be a basic thermostat that turns on heating if the temperature is below a certain threshold.

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2. Model-Based Reflex Agents

Model-based reflex agents are quite similar to simple reflex agents but with a more advanced decision-making mechanism. They not only rely on predefined rules but maintain an internal model of the world they operate in, allowing them to handle more complex tasks. They are commonly used in self-driving car technology, where they can collect and analyze data to make informed decisions while driving. For example, a self-driving car could use a model-based reflex approach to track the position of nearby vehicles and pedestrians over time.

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3. Goal-Based Agents

Rule-based agents or goal-based agents are designed to achieve specific goals or outcomes. They have more advanced reasoning capabilities compared to other agents and can evaluate different possibilities to reach their goals. Instead of just following predefined rules, these agents compare and select the most efficient path to reach their goal. This makes them suitable for more complex tasks like robotics or natural language processing, where the end goal is explicitly defined.

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4. Utility-Based Agents

Utility-based agents employ an advanced reasoning algorithm to assist users in maximizing their desired outcomes. These agents compare different scenarios and their expected utility and suggest the best course of action. In other words, they make decisions based on what will provide the highest level of satisfaction or benefit. They are used in complex environments where trade-offs are necessary, and actions must be chosen based on their expected outcomes.

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5. Learning Agents

Learning agents are built with the ability to learn from their past experiences and adapt to new situations. These agents use data and feedback loops to continuously improve their performance and make more accurate decisions over time. They consist of components like a learning element, performance element, critic, and problem generator. They adapt based on feedback from the environment, making them highly versatile. This type of agent is commonly used in e-commerce, where they learn from user behavior and preferences to provide a more personalized experience.

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AI Agent vs. AI Chatbot: What's the Key Difference?

At first glance, AI agents and chatbots may seem like two sides of the same coin, but they are actually quite different in terms of purpose and capability. AI agents are designed to perform autonomous tasks, while chatbots are primarily created for human interaction. One of the key distinctions between AI agents and AI chatbots is their ability to take independent actions.

Chatbots are programmed to respond to human input and provide assistance, but they lack the capability to take action on their own; their sole purpose is to assist humans. On the other hand, AI agents may not necessarily interact with humans at all, but they can independently receive a task and carry it out without any further input from a human. This versatility allows AI agents to operate in a wider range of environments, performing tasks that go beyond simple conversational interactions. 

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Example: AI Agents for Customer Service in Manufacturing Companies

Let’s look at an example from a B2B Industrial Manufacturing Customer Service Department to understand the differences more in detail. Imagine a B2B industrial manufacturer that supplies specialized machinery components to other businesses. The company uses both an AI chatbot and an AI agent to handle customer service tasks, but their roles and capabilities differ.

For the sake of our example, let’s assume a business customer requests a change in order and a refund for defective components.

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AI Chatbot:

• Purpose: The chatbot is integrated into the manufacturer’s online customer service portal. It assists clients by answering questions about products, order statuses, and providing troubleshooting advice for machinery components.
• Functionality: A purchasing manager from a client company contacts the chatbot to inquire about modifying an existing order for a batch of machinery components. Additionally, they want to request a refund for some components in a previous order that were found to be defective.
• Limitation: The chatbot can provide information on the process for modifying orders and can explain the company’s return and refund policy. It can guide the customer through the steps of filling out the necessary forms or direct them to the appropriate section of the customer portal. However, the chatbot cannot actually modify the order or process the refund. It can only escalate the issue to a human service representative.

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AI Agent:

• Purpose: The AI agent is designed to autonomously handle complex backend tasks such as order management, refund processing, and inventory adjustments without requiring human intervention.
• Functionality: Once the customer submits their request through the portal (e.g., after the chatbot has guided them), the AI agent takes over:some text
  • Order Modification: The AI agent reviews the current order details, checks inventory levels, and autonomously updates the order according to the customer's new specifications. It adjusts quantities, updates delivery schedules, and recalculates the total cost.
  • Refund Processing: The AI agent checks the records for the previous order, verifies the defect report, and applies the company’s warranty or return policy to approve the refund. It processes the refund through the financial system and updates the customer’s account balance.
  • Inventory and Quality Control: The AI agent also automatically flags the defective components in the inventory management system, triggering a quality control review. It may even suggest reordering or adjusting inventory levels based on the defects reported.
  • Customer Notification: After completing the tasks, the AI agent sends a detailed confirmation to the purchasing manager, outlining the changes made to the order, the refund processed, and any further actions taken (like the quality control review).

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However, despite their differences, AI agents and AI chatbots share some similarities in terms of the underlying technology they use.

• Natural Language Processing (NLP): Both AI agents and chatbots rely on NLP to understand human language and respond appropriately.
• Large Language Models (LLM): AI agents and chatbots can both use large language models (like OpenAI's GPT) to power their responses and interactions. LLMs are basically model architectures dealing with NLP.
• Vector Databases: To better understand human input, both AI agents and chatbots utilize vector databases and embeddings to store and analyze vast amounts of data.

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‍Applications of Artificial Intelligence (AI) Agents

AI agents (or intelligent agent) are making their way into various industries, changing the way businesses operate. Here are a few of the most common applications of AI agents:

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1. Autonomous Vehicles

One of the most futuristic uses of AI agents are self-driving cars and drones. These high-tech vehicles leverage AI agents to navigate and operate with minimal human intervention. They use sensors and cameras to perceive their surroundings and make real-time decisions (like when to stop, turn, or avoid obstacles). They can identify when the vehicle is approaching a traffic light/stop sign or adapt to changing road conditions.

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2. Customer Service

AI agents are among the most frequently implemented tools in the customer service industry. Since they can be integrated with company data, businesses can utilize them as customer assistants. AI agents can respond to customer queries, provide product information, and even assist in making purchases. They can also analyze customer data to identify patterns and improve the overall customer experience. This not only saves time and resources for the business but also increases customer satisfaction.

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3. Virtual Assistants

Virtual assistants like Siri, Alexa, Cortana, and Google Assistant are also examples of AI agents. They use natural language processing (NLP) to understand user commands and carry out tasks such as setting reminders, playing music, or providing information. These agents are becoming increasingly popular in smart homes, where they control connected devices like lights, thermostats, and security systems.

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4. Supply Chain Processes

In supply chain management, AI agents can be used to streamline and optimize processes. For example, AI agents can automate procurement tasks like purchase order confirmations or assist in sales and service tasks such as confirmation of sales orders. They can also help in inventory management by analyzing demand patterns and suggesting optimal stock levels. Additionally, AI agents can predict potential disruptions or delays in the supply chain and propose alternative solutions to minimize impact. This helps businesses save time and money while ensuring efficient and reliable supply chain operations.

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How Do AI Agents Change the Nature of Work?

While AI agents may conjure up images of futuristic robots taking over the world, the reality is not quite as dramatic - at least for now ;-). AI agents are here to assist and enhance human capabilities rather than replace them. An AI agent requires human triggers (commands, input, etc.) to understand their objectives, learn from data, and follow rules and restrictions when performing tasks. While the use of artificial intelligence continues to grow across industries like customer service, finance, and healthcare, AI agents aren't designed to replace human workers.

We'll likely see a rise in education and training for workers to integrate AI into their workflows, especially in industries that can automate some of the more routine and repetitive tasks (e.g. repetitive data entry in sales order processing or document analysis in compliance management). If done correctly, this upskilling will allow employees to reduce their time on manual tasks and to use their freed up time to think out of the box and focus on more complex, strategic tasks. This shift in work will create a more efficient, productive, and satisfied workforce. So, while AI agents may be the future of work, they are not a threat to human jobs. Instead, they are a tool that can help businesses and their teams work faster, smarter, and more effectively.

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How Can turian Help Streamline Your Business Operations?

If you've been considering implementing AI agents into your business processes, then turian is an excellent place to start. Our AI-powered assistants combine state-of-the-art AI algorithms, including Large Language Models, with custom business rules and advanced data querying techniques to achieve extraordinary speed and unmatched precision for task automation, a perfect combination for your business needs. With turian, you can automate various repetitive tasks like sales order entry, validation, purchase order management, document analysis, and more at a fraction of the time and cost.

Our AI agents are designed to understand context and nuances in natural language, just like humans. turian can respond to customers' free-text requests, write personalized emails, and even analyze lengthy conversation histories to provide accurate and relevant responses. turian has been built by a team of experts with deep industry knowledge to ensure our AI agents provide real value in your day-to-day operations.

But what truly sets turian apart is our commitment to customization and continuous learning. We understand that every business has its unique workflows and individual requirements, and we go the extra mile to tailor our AI agents accordingly. Our team works closely with each client to ensure our assistants seamlessly integrate into their existing systems and processes, providing a significant competitive edge. And as your business evolves, our AI agents evolve, too.

With continuous learning capabilities, our systems adapt and improve just like real employees do, making them an invaluable asset to any organization. And what's even better? Within less than two weeks, it can be up and running. You don't need to feed tons of your company data to train the AI; it's pre-trained and ready to go! turian can be seamlessly integrated into your email clients (such as Outlook or Gmail) and ERP/CRM systems (such as SAP, Oracle, MS Dynamics 365, etc.). Thanks to our intuitive user interface, you can easily monitor and manage all tasks performed by our AI agents. We're so confident in our technology that we offer a free Proof of Concept (PoC) with no strings attached, so you can see for yourself how turian can streamline your business operations.

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