What are AI agents?

How do AI agents work?

AI agents typically use machine learning (ML) and natural language processing (NLP) to tackle a range of tasks, from simple queries to complex problem-solving. Unlike traditional AI, AI agents can self-learn and continuously improve their performance.

AI agents follow a general cycle of perception, reasoning and action. The workflow typically defines the goal based on user input, breaks it into smaller subtasks, and executes the tasks using the knowledge base and available tools.

The following is a breakdown of how AI agents operate:

1. Define goals. The process is initiated when an AI autonomous agent receives precise instructions or goals from a user. This goal acts as the cornerstone for the agent's subsequent actions.
2. Gather and process data. An AI agent gathers instructions and discovers and processes data through various sensors, inputs or data sources. For example, an autonomous car uses sensors to collect data about the road, traffic and obstacles, while an AI chatbot collects user queries.
3. Implement tasks. The AI agent breaks down the goal into smaller, actionable tasks to ensure efficient and effective task execution.
4. Execute tasks. This step involves task execution, where the agent uses various tools and techniques, including large language models (LLMs) to automate tasks and manage complex cognitive activities.
5. Seek external feedback. Once a task is executed, it's removed from the list and the agent moves on to the next task. To assess progress toward the ultimate goal, the agent seeks external feedback and reviews its own logs. During this process, additional tasks might be generated and executed to achieve the desired outcome.

Difference between AI agents, non-agent chatbots and generative AI

AI agents, generative AI and chatbots are all forms of AI designed to assist users. However, they differ significantly in their capabilities, complexity and real-world applications.

The key features and distinctions among AI agents, traditional non-agent chatbots and generative AI include the following:

AI agents

• Functionality. AI agents are advanced systems capable of performing and adapting to a wide range of tasks autonomously. They're designed to augment human capabilities and can operate across various domains, not just customer service.
• Complexity. Agentic AI systems require more sophisticated technology, including ML and NLP, to understand context and perform tasks effectively. Since they can learn from interactions and improve over time, they're typically suitable for more complex applications.
• User experience. AI agents are conversational systems that deliver a dynamic, engaging user experience by handling multi-turn conversations and offering personalized responses based on user behavior and preferences. They can learn and respond to humans in a more natural, human-like manner.
• Investment costs. Setting up and running AI agents can require a higher initial investment and a skilled team to manage their learning and operational capabilities. For example, this typically includes purchasing or developing LLMs, acquiring necessary hardware and integrating the system into the existing infrastructure. Since agentic AI systems need large amounts of quality data for training and improving outcomes, additional costs can include data collection, storage and processing.

Non-agent chatbots

• Functionality. Non-agent chatbots are typically limited to predefined scripts and decision trees. They excel at handling simple queries and providing basic information, such as frequently asked questions, but their interactions are often linear and lack the depth and adaptability of AI agents.
• Complexity. Chatbots are simpler to execute, but they operate on a predefined set of rules and lack the intuitive understanding of human language. They're great for handling straightforward tasks but can struggle with complex or unexpected queries.
• User experience. Chatbot interactions can often feel rigid because of their scripted nature. This can also lead to less satisfying experiences when users ask questions outside their defined scope.
• Investment costs. Chatbots are generally easier and cheaper to deploy, making them a popular choice for businesses with limited technical resources.

Generative AI

• Functionality. Generative AI focuses on generating new information rather than responding to user queries or performing tasks autonomously. This includes generating text, images, music and artwork using models trained on vast data sets.
• Complexity. Generative AI models, such as Open AI ChatGPT often use deep learning techniques and large data sets to learn patterns and generate outputs. This requires significant computational resources and sophisticated training processes, making them inherently more complex.
• User experience. Generative AI offers a highly interactive experience, enabling users to engage in dynamic conversations that can adapt to their inputs. For instance, users can ask open-ended questions and receive detailed and contextually relevant responses.
• Investment costs. The investment required for generative AI can be substantial. Training and operating generative AI models, such as those based on LLMs, can cost millions. This includes expenses related to data acquisition, computational resources and ongoing maintenance.

What is the agent function and agent program in an AI agent architecture?

AI agent architecture is a structured framework that enables intelligent agents or systems to perceive, reason and act autonomously in their environments. The architecture can either be a physical architecture of a robot, such as actuators, sensors, motors and robotic arms, or a digital one, such as software agents or content generators that use text prompts, application programming interfaces and databases to enable autonomous operations. Both agent function and agent program are the main components that form the backbone of AI agent architectures.

Agent function

The agent function defines how an AI agent responds to its environment. For example, it maps the agent's perceptions or the data it receives from its environment to actions. Before designing the agent function, most developers evaluate the required information, AI capabilities, knowledge base, feedback mechanisms and other necessary technologies.

Agent program

An agent program brings an AI agent to life by building, training and putting it to work on a chosen system. It ensures that the agent performs as intended, meets technical standards and operates efficiently.

How can AI agents be utilized?

AI agents are used across various industries to enhance processes and automate tasks. They empower users by harnessing generative AI to not only assist but also collaborate on tasks.

Examples of AI agent use cases include the following:

• Customer support. Due to their diverse capabilities -- such as handling inquiries, managing refunds and providing advanced technical support -- AI agents are increasingly replacing traditional customer service chatbots. AI agents enable businesses to offer around-the-clock assistance without human intervention, ultimately improving customer satisfaction and reducing operational costs. AI agents can also be integrated into apps and websites to serve as virtual AI assistants to enhance customer experience.
• Finance. AI agents are transforming the finance industry by automating routine tasks such as risk assessment and transaction processing. By analyzing vast data sets, these agents provide valuable insights that help drive the strategic decision-making of financial operations.
• Healthcare. AI agents can handle and streamline healthcare-related tasks such as answering inquiries, scheduling appointments, reviewing insurance, generating medical summaries and approving care requests. They also accelerate drug discovery by analyzing biological data and predicting the efficacy of new drugs. Additionally, AI agents can personalize treatment plans, manage records and match patients to clinical trials, which empowers providers to deliver better care and improve outcomes. Multiagent systems are especially effective for solving problems in healthcare environments.
• Manufacturing. AI agents can streamline manufacturing processes by automating tasks, including quality control, predictive maintenance and supply chain optimization. For example, AI agents can analyze real-time data to identify potential issues, optimize production schedules and improve product quality.
• Insurance. In the insurance industry, AI agents are typically used to automate tasks such as processing claims, assessing risks and handling customer inquiries. By analyzing large volumes of data, these agents can provide personalized policy recommendations, detect fraud and streamline administrative processes.
• Autonomous vehicles. AI agents enable autonomous vehicles to operate with limited human intervention. These intelligent systems make informed decisions, such as when to turn or brake by perceiving the vehicle's surroundings. By using AI sensors, AI agents detect stop signs, navigate unfamiliar terrain and adapt to changing environmental conditions.
• Workplace automation. AI agents can automate routine business processes, enabling employees to focus on higher-value tasks. For example, these agents can automate data entry, scheduling and other repetitive and administrative tasks to streamline operations and boost productivity.

Benefits and limitations of AI agents

AI agents have numerous benefits and certain limitations. Balancing their advantages and drawbacks is essential for organizations seeking to utilize them effectively.

Benefits of AI agents

• Increased efficiency. AI agents automate repetitive tasks such as answering customer inquiries, scheduling appointments or processing claims. This provides workflow automation and frees human workers to focus on more complex tasks.
• Enhanced decision-making. AI-powered agents use ML algorithms to analyze vast amounts of data quickly, providing valuable insights that help businesses make informed decisions.
• Improved accuracy. AI agents reduce human error and improve task accuracy by following predefined rules and learning from large data sets that minimize mistakes caused by fatigue or bias. Additionally, by analyzing patterns and making data-driven decisions, they enhance the accuracy of certain tasks, such as data entry, diagnostics and financial analysis.
• Personalization. AI agents deliver personalized experiences by analyzing individual preferences and behaviors. For example, AI agents can provide tailored recommendations in retail and customized treatment plans in healthcare.
• High-quality responses. AI agents provide more comprehensive, accurate and personalized responses compared to traditional AI models, leading to a better customer experience. They achieve this by collaborating with other agents, utilizing external tools and learning from their interactions. It's important to note that these behaviors emerge naturally and aren't preprogrammed.
• Learning and adaptability. Many AI agents can learn and adapt over time to improve their performance based on feedback and new data, which leads to better outcomes.

Limitations of AI agents

• Limited understanding. Most AI agents rely on predefined rules, limiting their ability to handle complex or nuanced situations that require a deeper understanding of context.
• Issues with adaptability. While some AI agents can learn and adapt, their adaptability is often limited to specific environments or tasks, as they might struggle in dynamic or unpredictable situations.
• Ethical issues. Deep learning models can sometimes produce biased or inaccurate results. Human oversight is essential to safeguard the output of AI agents and mitigate these risks and ensure fair and helpful responses.
• Initial investment costs. Although AI agents can lead to long-term savings, the initial investment in technology and training of the AI agents can be significant, which can deter some organizations from adopting them.
• Infinite feedback loops. AI agents can encounter the issue of infinite feedback loops, where an agent's actions can unintentionally trigger a chain reaction that loops back to the original action, creating an endless cycle. For instance, an AI agent designed to optimize a system might execute a change that worsens performance instead of improving it, leading to a series of adjustments that only exacerbate the problem.
• Security and privacy concerns. The data AI agents use often involves sensitive personal or business information, which can raise concerns regarding data breaches, misuse and privacy violations.

The different types of AI agents

AI agents can be classified into various types based on their characteristics, functionalities and the complexity of tasks they can handle.

Common types of AI agents include the following:

• Simple reflex agents. These are the simplest agent types that operate on a set of predefined rules and don't possess any memory or the ability to learn from past experiences. They respond to specific stimuli in their environment and make decisions based solely on the current situation. For this reason, they're most suitable for straightforward and simplistic tasks.
• Model-based reflex agents. Unlike simple reflex agents, model-based reflex agents maintain an internal state that reflects the environment's current situation, enabling them to make informed decisions by considering both current and past inputs and adapting to changes.
• Goal-based agents. Also known as rule-based agents, these AI systems have enhanced reasoning capabilities. In addition to evaluating environmental data, they also compare different approaches to achieve the desired outcome. Goal-based agents always select the most efficient path and are well-suited for complex tasks such as NLP and robotics applications.
• Utility-based agents. These agents use utility functions to make decisions. They pursue goals and prioritize outcomes based on their perceived value. By evaluating the desirability of different states, they choose actions that maximize overall utility, making them suitable for complex environments where tradeoffs are inevitable.
• Learning agents. These agents improve their performance over time by learning from experience and past interactions. They use ML techniques to adapt to new situations, refine their decision-making processes and become more effective in completing tasks. For example, a virtual assistant can enhance its service by learning more about a customer's preferences.
• Hierarchical agents. These agents follow a hierarchical structure, where higher-level AI agents program and direct lower-level agents to work toward a shared goal. This setup enables businesses to break down complex, multistep processes into simpler tasks, with each AI agent focusing on a specific set of responsibilities.

How to effectively implement AI agents

AI agents have evolved well beyond virtual assistants such as Siri and Alexa and are proving extremely valuable in fields such as drug discovery in healthcare, fraud detection in finance and supply chain optimization in e-commerce. According to Forbes Advisor, approximately 64% of businesses anticipate AI will boost productivity.

Setting up AI agents effectively requires a strategic approach that considers various factors. The following key steps can help ensure a successful execution of AI agents:

1. Define clear objectives. Before setting up the AI agents, companies should start by identifying the specific goals they want the AI agent to achieve. Whether it's automating workflows, improving customer service or enhancing decision-making, having clear objectives guides the development and deployment of AI agents.
2. Gather and prepare data. AI agents rely on both structured and unstructured data to function effectively. Therefore, organizations must ensure they have access to high-quality data that can provide context for the AI agent's tasks. For example, this can include knowledge articles for complex queries and structured data for personalized interactions.
3. Select the agent type. Organizations seeking to build an AI agent should choose the most suitable type for their needs. For instance, a reactive agent might suffice for routine customer queries, while more complex tasks requiring adaptability and learning would benefit from a goal-oriented or learning agent that can offer more sophisticated support.
4. Integrate with existing systems. When building an AI agent, it's crucial that it seamlessly integrates with existing systems such as customer relationship management and customer service tools. This integration enables the AI agent to access relevant data and provide better support to users.
5. Assemble the development team. The next step is to assemble an AI development team responsible for gathering the data to train the AI agent. The choice of programming languages, platforms and other technologies will depend on the team's skills and expertise. ML engineers, data scientists, DevOps engineers, and user interface and user experience designers are a few roles that should be part of the development team.
6. Select tools and technologies. It's important to choose the right tech stack. This can include selecting the appropriate technologies, such as ML frameworks, programming languages such as Python and Java, and AI tools for data processing and model building.
7. Design the AI agent. The agent's architecture should define how it will interact with users, access data and perform various tasks. For more complex agents, this can involve creating a hierarchical structure where higher-level agents manage and direct lower-level agents.
8. Train the AI agent. This step involves using the collected data to train the AI agent. It requires feeding data into ML models, enabling the agent to learn patterns, make predictions and refine its decision-making abilities.
9. Test and deploy the AI agent. The agent should be thoroughly tested in a controlled environment to assess its performance in various scenarios. Iterative testing helps identify and address issues. Once fully trained and tested, the agent can be deployed in its intended environment, such as a website or customer support system.
10. Monitor and improve the agent. Lastly, it's crucial to continuously monitor the AI agent's performance, gather feedback and analyze its outcomes. This data should be used to make improvements and updates, ensuring the agent adapts to changes in user behavior and the business environment.

AI agent vendors

Numerous vendor platforms and tools are available for building AI agents, including the following:

• AgentGPT. AgentGPT enables users to create, configure and deploy autonomous AI agents directly in their browser without requiring extensive programming knowledge. Built on OpenAI's GPT-3.5 and GPT-4 models, the platform uses the models' advanced capabilities to generate human-like text and autonomously perform a range of tasks.
• Amazon SageMaker. The SageMaker fully managed service provides developers and data scientists with tools for building, training and deploying ML models, including AI agents in a production-ready environment. It also offers customizable ML algorithms and infrastructure for scaling.
• Google Cloud Vertex AI. The Vertex AI Agent Builder, which is part of the Google Cloud Vertex AI platform, helps simplify the process of creating autonomous and intelligent agents, enabling both technical and nontechnical users to build them.
• LangChain. LangChain is a powerful library designed for Python, JavaScript and TypeScript that facilitates the rapid prototyping of applications powered by LLMs. It enables developers to chain together various LLM tasks, which is essential for building complex AI agents.
• Microsoft AutoGen. The AutoGen open source framework helps simplify the process of building and managing AI agents, enabling them to collaborate and perform tasks autonomously or with human oversight. Through AutoGen, multiple AI agents can work together to solve complex tasks. It can also use powerful language models such as GPT-4 to enhance agent capabilities.
• Salesforce Agentforce. The Agentforce platform is designed to create and deploy autonomous AI agents that can support users in various business functions, including sales, service, marketing and commerce. The platform's low-code Agent Builder helps users define and customize AI agents using natural language queries.

From speeding up decision-making processes to transforming sectors -- including healthcare, finance and education -- AI promises both significant opportunities and challenges. Explore how AI will shape the future, along with the potential risks and benefits of this technological revolution.