What Are AI Agents?

If you’re someone who finds AI fascinating but nebulous, you’ll be intrigued by AI agents. Those large language models (LLMs) that companies have invested billions in? They’re getting real jobs as the brains behind AI agents: What if chatbots could understand your HR policies and have nuanced discussions with employees about them? What if a fraud detection system could act autonomously to shut down bad transactions as they’re happening? What if you could give an AI system a goal and it would autonomously do what it took to achieve it?

All these use cases are possible with AI agents.

You can even equip agents with tools—algorithms, sensory inputs, data sources, and even access to other agents—so they can perform complex tasks under their own steam. Think of a warehouse robot that navigates aisles to check inventory by combining information from a range of sensors, cameras, and scanners with its control software and an ERP inventory management system.

What’s being called “agentic AI” is coalescing as an exciting opportunity for all sorts of organizations by making AI easy to use and vastly more helpful.

What Is AI?

AI, or artificial intelligence, refers to computing systems that are trained to simulate human intelligence. Most AI systems are programmed to learn, and some can improve their performance based on experiences and new data, solve problems using a wide range of inputs, and pursue goals and objectives in a methodical manner. In the most recent advancement, generative AI systems can make decisions and initiate actions independently to reach their goals. GenAI is used in applications as varied as self-driving cars, media recommendation engines, and tools such as DALL-E and Midjourney that create images based on textual prompts.

Enterprise AI refers to ongoing work to apply GenAI and related technologies to business workloads, with systems augmented with the organization’s data. Think customer service, personalized marketing, and HR and finance assistants.

What Are AI Agents?

AI agents are software entities that can be assigned tasks, examine their environments, take actions as prescribed by their roles, and adjust based on their experiences.

People give AI agents objectives based on the agent’s role and the organization’s needs. With its objective in hand, the agent may make plans, perform task, and pursue the goal based on its training, the application in which it’s embedded, and the wider environment in which it operates. Agents learn and iterate and may take on specific roles, connect with data sources, and make decisions on their own. Advanced agents have specialized jobs that may involve executing multistep processes that require judgment, communicate in a way that mimics human interactions, and often cooperate with other agents. The modular nature of agents enables complex workflows. The autonomy given to agents is determined by the humans who invoke them. Just as in hiring a new assistant, more autonomy may be given as proficiency is proven.

Agents work by combining natural language processing, machine learning capabilities, an ability to gather data by querying other tools and systems, and continuous learning to respond to questions and perform tasks. A good example is a customer service AI agent. When a customer inquiring about an order asks, “Where’s my stuff?” the agent forms its response by checking with the order processing system, querying the shipping carrier’s tracking system via an API, and gathering information on potential weather or other external factors that could delay delivery.

The term agentic AI refers to systems that actively pursue goals and objectives versus performing a simple task or responding to a query. Agentic systems can often initiate actions, such as a customer service AI proactively sending a query to a carrier to ask about shipping delays.

One way to make agents more useful is by incorporating retrieval-augmented generation, or RAG, a technique that lets large language models use external data sources specific to the organization or agent role. RAG allows agents to find and incorporate up-to-date and relevant information from external databases, enterprise systems such as an ERP, or documents into their responses, making them more informative, accurate, and relevant for the audience. For example, an IT support agent could consider past interactions with customers before it decides how best to address the issue at hand. It might include in its response links to helpful documentation or decide to open a ticket on the customer’s behalf if the issue needs to be escalated.

Key Takeaways

• AI agents are proactive planners: They work to identify the steps needed to achieve the desired goal.
• As with any AI technology, AI agents can deliver benefits commensurate with their training and the data they can draw on and the limits humans set for their operation.
• Goals that are clearly defined, achievable, measurable, and quantifiable are essential for AI agent success.
• The steps to implement an agent are similar to any AI deployment and begin with clearly defining the task parameters.

AI Agents Explained

An AI agent is a software entity that can perceive its environment, take actions, and learn from its experiences. Think of it as a digital assistant or a robot that can perform tasks autonomously based on human direction. AI agents have distinguishing characteristics, notably the ability to set goals, gather information, and use logic to plan out steps to achieve their objectives. Because they’re underpinned by LLMs that provide the intelligence to understand the intent behind queries, AI agents aren’t dependent on keywords, scripts, or preconfigured semantics. Rather, they can draw on data retained from previous tasks, combined with chat-based prompts, to dynamically come up with solutions.

AI agents also learn by trial and error. Reinforcement learning is where an AI model refines its decision-making process based on positive, neutral, and negative responses. They mimic human ingenuity and can use tools, including cloud-based and enterprise applications and data sources, APIs, and other agents, to achieve their goals. They may also use additional AI- and machine learning-based systems to analyze complex data, natural language processing tools to process inputs, RAG to provide up-to-date and contextually appropriate content, and cloud services for the computational resources required to do their work.

How Do AI Agents Work?

AI agents work by combining techniques and technologies, such as those we just noted, to achieve their assigned goals. For example, a recommendation agent might use machine learning, tapping massive data sets to identify patterns; natural language processing to understand requests and communicate with users; and interfaces to enterprise tools, such as an ERP system, database, or Internet of Things sensors, or external data sources, including the internet, to gather information.

AI agents are planners. They can identify the tasks and steps needed to achieve the assigned goal. For our customer service agent, understanding where a given shipment is requires a series of actions. It would first access databases with information about the specific order, such as the shipment ID, delivery method, and date placed. Next, it would use that data to query the shipping carrier’s database using a web services interface to provide real-time tracking and an estimated delivery date. The agent could also look at where the shipment currently is and how long it has taken in the past to make the next leg of its journey. If it’s in an air freight terminal in Boston and a hurricane is moving up the East Coast, the agent might infer that a delay is likely and convey that information to the customer.

Benefits of AI Agents

AI agents, like any AI technology, can deliver benefits commensurate with their training and the data they have to draw on. A feature that separates agents from their more static predecessors is that they can recognize when they don’t have enough data to make a high-quality decision and take action to get more or better data. The formulation of agents within applications is a highly applied version of AI. As such, organizations will find that for success with agents, AI gurus aren’t needed so much as those who understand business processes and, possibly, data quality experts. These specialists can help define agent objectives, set parameters, and assess whether business goals are met, calling in IT or the software vendor only if they believe the AI itself is malfunctioning.

Specific benefits cited by early adopters of AI agents include

• 24/7 availability. AI agents can operate continuously, without downtime. And when delivered from the cloud, agents can operate anywhere that customers, employees, or other intended users might be.
• Accuracy. AI agents can minimize human error when performing repetitive tasks, and the vast amounts of data they can draw on lead to more accurate and well-informed decisions. Of course, that’s predicated on them having access to accurate, up-to-date, and complete data sources. Unlike first-generation GenAI tools, agents can better recognize when they don’t have enough information to make a quality decision and seek more data as needed.
• Consistency. AI agents can be made to follow prescriptive processes and procedures, helping ensure tasks are performed the same way every time. Agents can also minimize variations stemming from human fatigue or differences in how various employees might execute a process.
• Cost savings. While AI agents may reduce operational costs by automating repetitive tasks once performed by humans, they can also discover and suggest ways to optimize processes while reducing errors that can cost the company money.
• Data analysis. AI agents can process and interpret massive data sets for analysis activities, including long-range planning, fraud detection, and predictive maintenance to head off equipment failures. In cases where the agent can’t analyze data for whatever reason, it can invoke other tools to do the job.
• Efficiency. AI agents can automate tasks and processes, freeing up human employees to focus on more complex and strategic activities. And they don’t require vacations.
• Personalization. With AI-powered marketing campaigns created by agents, companies can effectively target specific customer segments, often resulting in higher conversion rates and lower marketing costs. On a macro level, personalization is a trend for a reason—many consumers like when companies remember and use their buying histories, preferences, and personal information.
• Scalability. While scaling up the use of AI agents can take time, it can be easier and less costly than adding new human resources. Expand the role of agents at a deliberate pace, assessing the quality of work with each new task given to them. Consider the data and other resources available to an agent and whether they’re sufficient to achieve a new goal. And don’t forget training: Employees need to be educated on how to get the most out of the agents they’ll use.

Challenges of AI Agents

AI agents can be challenging to develop and put into production primarily because they rely on complex models, powerful compute infrastructure, and vast amounts of data that must be curated and kept up-to-date. Moreover, IT talent oversight is required to confirm that agents can effectively interact with humans and adapt to unexpected situations, and business and data experts need to help with setup. Make sure you have expertise in natural language processing and machine learning and watch for these problems.

• Bias. If a generative AI model informing the agent is trained on biased data that includes gaps in perspectives or harmful or prejudicial content, those biases may be reflected in its output. For example, if a business includes gendered language in its job postings, an HR agent model could facilitate the preference for candidates of one sex over another. By narrowing what agents do and the range of questions or tasks they’ll take on, bias can be substantially reduced as training sets can be more precisely honed to the envisioned use of the agent.
• Adaptability. Although agents are designed to learn and improve over time, they can encounter challenges when faced with a rapidly changing environment or unexpected requests or outcomes. A common culprit is overfitting, a common AI training challenge where models become too attuned to the data they were trained on, making it difficult to incorporate new data. Agents are therefore limited in their scope. The notion that it’s never a good idea to ask a brain surgeon to do plumbing applies to agents as well.
• Complexity. While AI agents that focus on specific, fairly rote tasks may be relatively simple to use, as the assigned tasks get more sophisticated and demand a wide range of functions, agents may become difficult to design, implement, and maintain. As with any new endeavor, adopting agents is best accomplished with small, incremental steps.
• Dependence on data. Like all AI, agents require high-quality data to perform well. AI agents that are integrated with other systems, such as human capital management or ERP, have an edge since these systems inherently accumulate high-quality data, but they may need to be tuned to handle interactions and data exchange. Organizations need to make sure the data sources that agents draw from are accurate, timely, and available.
• Interpretability. First-generation GenAI systems operated as “black boxes,” making their output hard to parse. Agents are designed to better explain how decisions are made and which data was considered in making the decision. Starting with simple tasks and moving on to more complicated jobs only when the basics have been mastered can help business pros understand how the agent is doing its work. Further, when agents get it wrong, they can learn from corrections made by business experts. Those interactions contribute to a better understanding of how they do their work.
• Resource-intensive operations. Like all AI, agents demand significant computational power and storage. When agents are part of applications delivered from the cloud, it’s up to the provider to resource systems appropriately and deliver adequate performance. On-premises applications will need IT to ensure sufficient resources.
• Security risks. To provide the service that business professionals need, agents must access company-proprietary information. Further, because agents can remember at least the outcomes of transactions, it’s important to help ensure agents don’t provide undesired access to sensitive data. Since most agents will be delivered within business applications, it’s incumbent on the software provider to prevent them from leaking proprietary data. Still, agents represent a new avenue of attack for bad actors and a new skill set for enterprise security teams, which need to continually assess whether data loss is possible.

Components of AI Agents

AI agents depend on a range of inputs to do their work, with the specific mix depending on agent type. A customer support agent will converse with customers, consult their purchase and support histories, and access support libraries to answer questions. Some agents will interact only with other agents. A database query agent might create SQL queries to retrieve information requested by other agents. Agents functioning as virtual assistants measure success by how well they accomplish tasks, often based on human feedback. All require a unique mix of components.

• Action. Actuators or interfaces enable agents to interact with their environments. Actions might be physical, such as turning a knob, piloting a self-driving vehicle, or controlling a robotic arm; cognitive, perhaps deciding between several options for an opening gambit or creating a list of possible ways to achieve checkmate; or communicative, including composing an email, transcribing audio, or asking and answering questions.
• Goals/utility. Goals and utility are related. Goals define the desired outcome for the agent, such as an HR assistant successfully crafting a job description with input from a recruiter and hiring manager. Utility measures how well the agent achieves its goals and may be represented by a numerical value. A gaming agent will measure utility by matches won, while an autonomous vehicle’s utility is largely based on its safety record and rider scores.
• Learning. AI agents can improve their results by incorporating lessons from tasks accomplished. An LLM’s learning stops when its training stops, but by observing which combinations of proprietary data and questions produce the best results, an agent can become better at tasks over time. Agents may also acquire new knowledge from additional training, whether supervised, unsupervised, or reinforcement learning. The recruiter might rate the agent on the job description it produced, adding to its utility score; the agent then uses that data to guide future writing.
• Memory. This refers to the agent’s ability to store information from past experiences and retrieve and use it to make more-informed decisions and adapt to changing circumstances. Memory is essential for AI agents to improve their performance over time.
• Perception. AI agents may use sensors or other mechanisms to gather and perceive information from their environments. Think about a camera to recognize objects and detect patterns or a microphone to capture and process spoken queries. Agents may also use sensors to help manipulate objects or navigate their own positions in the physical world.
• Reasoning. Logical decision-making based on data, rules, probability, and learned patterns is fundamental for an AI agent. Reasoning is what enables an agent to identify multiple different options and decide on the optimal course of action based on available information and outcome criteria.

Types of AI Agents

1. Simple reflex agents. These agents operate based on a set of condition/action rules and react to inputs without considering the broader context or history. An example is a basic chatbot programmed to respond to predefined keywords or phrases but unable to understand context or engage in an expansive conversation.
2. Model-based reflex agents. These agents have internal models of the environment relevant to their functions, allowing them to consider the current situation and the effects of various actions before deciding what to do. Self-driving cars are a good example. Their “world” is the immediate roadway around them. They need to track the movements of things within their world and make decisions about how fast they can travel and whether they need to brake or take evasive action when objects move toward them.
3. Goal-based agents. These agents build on the capabilities of reflex agents by considering long-term objectives and planning their actions accordingly. They have a more sophisticated decision-making process than reflex agents. For example, a chess or go agent needs to look several moves ahead and have a strategy to win, which may include making sacrifices in the short run.
4. Utility-based agents. These agents make decisions based on maximizing desired utility—that is, the measure of how successfully the AI agent achieves its goals over time. This means they choose actions more strategically, selecting those most likely to lead to positive outcomes or minimize negative ones in the longer term. They aim to maximize satisfaction or benefits even when faced with competing goals by striking a balance. Where a goal-based agent might seek to win a game, a utility-based agent will try to continuously optimize for an ongoing objective, like minimizing energy use or maximizing sales of a high-margin product.
5. Learning agents. These agents hone their performance over time by ingesting new data and refining responses based on interactions with users. Recommendation engines are learning agents. Accuracy improves over time, whether the agent is suggesting movies and TV shows, music, or items a consumer may wish to purchase.

AI Agent Use Cases

Ideal AI agent use cases generally have related data and other systems, such as a CRM or ERP, that AI agents rely on. They’re also task-oriented: Think answering a customer question or driving a passenger from point A to point B. Look for jobs that take advantage of agents’ ability to improve their performance over time and to make decisions based on their understanding of their environments and assigned goals.

Current popular use cases include

• Autonomous vehicles. Self-driving cars navigate and make decisions based on their surroundings.
• Content recommendation. Personalized suggestions on platforms like Netflix or YouTube can increase the stickiness of these products.
• Customer support. Automated chatbots for answering customer inquiries that can go beyond pre-formed answers are key to customer satisfaction.
• Finance. Agents used by financial services firms include automated trading systems and fraud detection.
• Gaming. One example is agents that act as NPCs, or non-player characters, with adaptive behavior that can help video game developers focus more on primary plotlines.
• Healthcare. AI agents that help diagnose medical conditions or managing patient care are trained with (usually anonymized) patient records and medical images that teach them to identify patterns so they can predict outcomes and risk factors and suggest courses of action.
• Personal assistants. Virtual assistants, such as Siri or Google Assistant, are examples of agents that learn via customer interactions.
• Retail. The options in retail are nearly endless. As one example, Neostar offers a platform for buying, selling, and servicing used cars. It uses an agent to power individualized communications to customers, with recommended product listings to highlight vehicle suggestions in email messages that reengage customers and drive them back to Neostar’s website.
• Robotics. AI-driven robots can be controlled by agents that perceive their environments, make decisions, and take actions. Robots used in manufacturing and assembly lines, for example, often rely on AI agents to perform tasks that include picking, packing, and quality control.
• Smart homes. Managing home automation systems and answering verbal questions are popular jobs for agents, as are powering security cameras, doorbells, and alarms that use AI to detect and respond to potential threats.
• Supply chain management. Optimizing logistics might include using agents to analyze inventory data to identify slow-moving items and detect changes in demand patterns and adjust inventory levels accordingly, that can reduce holding costs.

6 AI Agent Best Practices

As with any technology investment, you want your AI agents to cost-effectively deliver the desired functionality now and in the future. For agents embedded within applications, best practices are similar to those you’d use for a new employee, such as carefully monitoring early outputs and ramping up the complexity of work as the employee masters each assigned task.

For organizations looking to create their own agents suited to unique needs, the process is more involved. Consider these six requirements and possible best practices to address them.

1. Establish clear objectives. Clearly defined, achievable, measurable, and quantified goals are essential for AI agents. As with a human employee, if the agent doesn’t understand expectations, it’s unlikely to meet them. Keys to success include keeping objectives well-defined and specific. Avoid vague or ambiguous goals, aiming instead for objectives that are achievable given the AI agent’s capabilities and resources. Define KPIs to measure success and use that data to improve the model.
2. Continuous learning. Continuously fine-tuning an LLM at the heart of an agent isn’t practical but refining the data it uses to make decisions and complete tasks is. For agents embedded in applications, it will be up to the vendor to decide when it’s time to refine the training of the LLMs powering its systems. It’ll also be up to the vendor to refine how interactions with the agent are stored and recalled for facilitating the agent’s memory of past work.
   
   In bespoke agents, refining memory techniques and the data and other inputs supplied can happen more frequently than fine-tuning of the LLMs themselves. For those building their own agents, these processes will need to be worked out before the agent is ready for use and likely tweaked to optimize the agent’s operation.
3. Documentation. Comprehensive documentation is essential for understanding, maintaining, and improving AI agents. There are three main types of documentation to consider.
   • Technical documents might include diagrams of the AI agent’s components, data flow, and decision-making processes along with records of any new code required for the AI agent’s functionality; the algorithms and models used; and the data used to operate the AI agent, including sources. How will business users request changes and provide feedback?
   • Operational documentation includes manuals for users on how to interact with the AI agent; guidelines for IT to maintain the AI agent, including troubleshooting; and instructions for integrating agents with the data sources they need to function.
   • Legal and compliance records should include an evaluation of the AI agent’s potential impact on employee and customer privacy and documentation of compliance with relevant laws and regulations.
   In addition, track and share the KPIs you’re using to measure the AI agent’s performance, and graph results over time.
4. Human oversight. Just like a new employee, agents will need time to learn about your organization and its practices. You’ll also want to go slow in terms of giving agents tasks and monitoring the outcomes. Provide extensive oversight until team members are confident the agent can work autonomously. Assign oversight roles to individuals or teams, operating under a governance structure, and make sure your human in the loop system allows for intervention and that the agent incorporates and prioritizes human feedback. Set up accountability guidelines defining who’s responsible for the agent’s actions.
5. Robust testing. Thoroughly vet the agent in diverse scenarios, and focus on validation testing to gauge performance against benchmarks or real-world process outcomes. It’s best practice to test all the components of the agent individually and then watch how they interact, to the extent possible. Also make sure the agent is drawing data from relevant external systems, such as an ERP or database, without bottlenecks. Finally, do UX testing with actual users of the system.
6. Security measures. Protect the agent from unauthorized access and attacks by encrypting and, where appropriate, anonymizing data used by the agent. Robust access controls are also critical. Your network and infrastructure security, secure coding, monitoring and incident response, and audit practices should extend to your AI systems.

Implementing AI Agents

The steps to implement an agent are similar to any AI deployment. First, you’ll define the task: What do you want the agent to do, being as specific as possible with goals and objectives. Then, identify the functional process the agent will follow, the data it’ll need to access, the relevant business experts, and the tools and other agents it can access as part of its work.

Best practices include assigning a small beta test group, closely monitoring use and outcomes, tuning the agent based on results, and increasing autonomy based on proven success. Where applicable, you might model the process on provisioning a new employee. Let’s consider a demand forecasting agent coming online to help a retailer plan for the back-to-school season.

1. Have a defined job description. The AI agent is expected to forecast demand for products, including backpacks, notebooks, and children’s apparel.
2. Decide what data is required. Curate data sources to set your agent up for success. Our demand forecaster will need, at minimum, past sales figures for the products being forecasted; information about current market trends and economic indicators; and customer demographics, preferences, and purchase histories. Adding data on seasonal patterns that may affect demand, like expected higher than normal temperatures, and historical details on successful promotions, discounts, and marketing activities will help increase accuracy.
3. Introduce helpers. Integrating the AI agent with other systems, such as inventory management, your ERP, and supply chain planning tools, will help enhance its effectiveness. You’ll also want to identify human experts in the relevant product lines who can provide valuable insights and help the AI agent make more accurate predictions.
4. Provide feedback. Regular evaluation and tuning is an upfront time investment that often proves worthwhile. Gather feedback from customers and your experts to identify areas for improvement, and work with the software provider to adjust as needed.

One note: You should have sufficient computational resources to run the AI agent—laggy performance will kill enthusiasm before the project gets off the ground.

AI Agent Examples

These are just some of the AI agents currently available. Organizations should look at their pain points: What roles are you having trouble filling? What are some opportunities you’ve identified but lack the resources to test out your hypothesis? Is there a persistent employee or customer complaint that might be addressed by AI? Also, talk to your cloud and enterprise application providers to see what agents they’re baking into their products and services. Those roadmaps can spur ideas.

Examples of AI agents include

• Conversational agents interact with the outside world. In the case of enterprise applications, interactions are usually with humans, but they could be with another software program. In industrial settings, for example, conversational agents may interact with manufacturing equipment or Internet of Things devices.
• Functional agents, also called user-proxy agents, are associated with a particular organizational persona or role. Using a real-world example, you may encounter several “functional agents” when you go for your annual physical: The receptionist agent checks you in and the nurse agent takes basic vitals, such as your weight and blood pressure. Finally, you see the physician, the doctor who conducts a more detailed exam, assisted by an agent that summarizes the visit and generates necessary paperwork. Each of these agents performs specific subtasks with specific expertise using different tools, all communicating with one another as needed to accomplish a task.
  
  Examples of functional agents include
  • Hiring manager agent. Performs tasks including documenting requirements—for example, candidate skills and experience—for hiring decisions and reviewing job postings created by other GenAI systems for accuracy.
  • Field service agent. Provides information to technicians, automates tasks such as scheduling, helps with diagnostics, and makes other decisions for more efficient field service workflows.
  • Receivables clerk agent. Streamlines payment processing; takes actions to improve cash flow, such as initiating dunning procedures; and produces reports on receivables performance.
  • Customer support agent. Augments customer support functions by providing relevant information to human support agents or customers.
• Supervisory agents are the orchestra leaders. These agents direct other agents and drive the planning and reasoning needed to achieve an objective. One example is a user-proxy agent that makes decisions on whether to act on behalf of a human or connect with a person for human-in-the-loop feedback.
• Utility agents, also called task-based agents, are usually associated with a specific function and are called on by other agents to perform a task, such as querying a database, sending an email, performing a calculation, or retrieving a document. Utility agents deployed as part of a complex workflow usually act autonomously because of their low-risk functionality. Examples include
  • Coding agent. Writes code to perform a specific task using languages like HTML, Java, or Python.
  • Conversational agent. Receives tasks from humans and communicates the results of workflow tasks in the manner best suited to the task’s requester.
  • Copy generating agent. Summarizes a body of text or generates sample text to use as a starting point for longer communications.
  • Database query agent. Performs tasks related to data retrieval, such as making SQL queries.
  • RAG agent. Coordinates the retrieval of specific, up-to-date data necessary for an LLM to make a proper response to a prompt or carry out a task.
  • Scheduler agent. Schedules meetings with stakeholders to advance a project.
  • Search agent. Determines the optimal type of search, for example, a web or document search, and calls the appropriate tool to perform the task.
  • Skills enrichment agent. Uses documentation to suggest the skills needed to complete tasks, such as creating a job posting or assisting an employee with profile creation.

Increase Efficiency with OCI Generative AI Agents

OCI Generative AI Agents combines the power of LLMs and RAG so that employees, partners, and customers can directly query diverse knowledge bases enriched with your enterprise data. Quickly create and embed custom AI agents in your enterprise business applications and processes.

The service provides up-to-date information through a natural language interface and the ability to act directly on it. Looking to try AI agent technology? OCI Generative AI RAG Agent—the first in a series of Oracle AI agents—is generally available.

Most of us have asked a chatbot a question and received a response that didn’t solve the problem. Ending that frustration is the ultimate goal of smart AI agents. Giving people contextually accurate and relevant information is good for them and for your organization.

AI Agents FAQs

Is ChatGPT an AI agent?

Yes, ChatGPT is an AI agent. It’s an LLM that’s designed to interact with users in a conversational way. As an agent, ChatGPT’s objective is to write creative content, generate human-quality translations and code, and answer questions accurately. The ChatGPT LLM can also be used as the basis for other agents.

What are the types of AI agents?

Types of AI agents include simple reflex, model-based reflex, goal-based, utility-based, and learning agents.

1. Simple reflex agents operate based on a set of condition/action rules and react to inputs without considering the broader context.
2. Model-based reflex agents have an internal model of the environment relevant to their function, allowing them to consider the current situation and the effects of various actions before deciding what to do.
3. Goal-based agents build on the capabilities of reflex agents by considering long-term objectives and planning their actions accordingly.
4. Utility-based agents are associated with a specific function and are called on by other agents to perform a task, such as querying a database, sending an email, performing a calculation, or retrieving a document.
5. Learning agents hone their performance over time by ingesting new data and refining responses based on interactions with users.

What are real-life examples of agents in AI?

Real-life early examples of AI agents are Alexa, Google Assistant, and Siri, virtual assistants that can perform tasks including setting alarms, sending messages, and searching for information. For businesses, Oracle Digital Assistant is a conversational AI platform that lets businesses create chatbots and virtual assistants for customer service and other applications—essentially an AI agent that helps companies create their own agents.