What are AI Agents? A Complete Guide

Maybe you’re dealing with a surge in case volume, but your team is stretched thin. Or you’d love to offer 24/7 support, but tight budgets make it difficult to justify a round-the-clock workforce.

Or you’re under pressure to reduce response times, yet customers just keep expecting more — and want everything faster. That’s where AI agents like Agentforce come into play.

Let’s review what AI agents are, how they work, and what you can do to deploy them successfully.

• What are AI agents?
• How do AI agents work?
• What is AI agent architecture?
• Types of AI agent architectures
• What are the benefits of AI agents?
• How do businesses build and deploy AI agents?
• How to implement AI agents: 8 tips for success
• Why Service Cloud for AI agents?

An artificial intelligence agent is an intelligent system that can understand and respond to customer inquiries without human intervention. They rely on machine learning and natural language processing (NLP) to handle a wide range of tasks, from answering simple questions to resolving complex issues to even multi-tasking.

Most importantly, AI agents can continuously improve their own performance through self-learning. This is distinct from traditional AI, which requires human input for specific tasks.

These days, 83% of customers cite good service as their most important criterion when deciding whether to make a purchase. It’s not enough to offer a compelling product in the current competitive landscape. Customers want great service, and they want it readily available at every touchpoint.

The problem? 77% of companies struggle to provide a cohesive customer experience throughout the entire value chain. The complexity of managing multiple channels, sorting through siloed data sources and communicating across departments make it difficult for companies to deliver a seamless experience for every customer.

This is where AI agents come in clutch. Their ability to automate tasks and 'learn on the job' means businesses no longer need to rely solely on employee communication to provide the optimal customer experience.

Consider an AI agent that can assist with personalised treatment plans or an agent that provides personalised content recommendations based on a user's viewing habits. AI agents can even monitor customer interactions in real-time and provide actionable support to sales reps. All of this saves time and cuts costs while allowing businesses to provide a better service to their customers. (back to top)

It’s a common misconception that an AI agent is another word for a ‘chatbot’, but this isn’t the case. An AI chatbot is a simpler automated AI bot that responds to user queries. They treat customer engagements like a game of tennis, reading each response before ‘returning the ball’ with an appropriate reply.

In contrast, an AI agent is much more complex. It can understand social cues and context. It can think freely and use its decision-making mechanism to decide on the best approach based on a nuanced understanding of the situation.

A chatbot is reactive—it follows strict scripts and dialogue patterns, which can frustrate customers, especially if the chatbot doesn’t understand the user’s query. An AI agent, on the other hand, can detect emotion and understand intent, allowing it to adapt to any situation and create more personalised customer experiences.

AI Agents can also handle a much wider variety of tasks. Whereas a chatbot is only helpful for handling common questions, AI agents can multi-task and offer comprehensive solutions in almost any industry.

Beyond providing excellent customer service, AI agents can automate repetitive tasks like data wrangling, application processing, and scheduling, freeing up employees to work on high-value tasks. They can even make data-backed decisions in context, such as providing a user with personal finance recommendations or helping a doctor diagnose a patient.

All in all, AI agents can do everything chatbots can and a lot more — and they can do it better.

Here’s a breakdown of how AI agents operate:

• Perception and data collection. AI agents start by gathering data from a variety of sources including customer interactions, transaction histories, and social media. This data is crucial for understanding the context and nuances of customer queries. Advanced AI agents can integrate and process data in real-time, providing them with the most up-to-date information to handle inquiries effectively.
• Decision-making. Using sophisticated deep learning models, AI agents analyse the collected data to identify patterns and make decisions. For example, they can determine the most appropriate response to a customer query based on past interactions and the current context. The agent's ability to learn from previous experiences and refine its responses over time enhances this decision-making process.
• Action execution: AI agents can execute the required action once a decision is made. This could involve answering a customer query, processing a request, or escalating a complex issue to a human agent. The execution is designed to be seamless and efficient, ensuring that customers receive timely and accurate responses.
• Learning and adaptation: AI agents continuously learn from each interaction, refining their algorithms to improve accuracy and effectiveness. They update their knowledge base and use feedback to enhance future interactions. This continuous learning capability ensures that AI agents remain effective and relevant, even as customer expectations and business environments change.

By combining these capabilities, AI agents can handle a wide range of customer service tasks autonomously, such as making product recommendations, troubleshooting problems, and engaging in follow-up interactions. This frees up human agents to focus on more complex and value-added activities.

As you can see, AI agents have the power to be incredibly helpful for organisations. But to completely understand how these agents work, we need to dive deeper into their foundations. Let’s examine AI agent architectures in more detail.

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AI agent architecture acts as the foundation for an AI agent’s components, principles, and interactions. It’s essentially a structural framework that determines how the agent makes decisions, processes information, and perceives its environment. It consists of four core components:

• The profiling module: Determines the role or function of the agent within a specific context. Think of this as the agent’s eyes and ears.
• The memory module: Enables the agent to remember details, recall past behaviours and learn from mistakes. This is the knowledge base of the agent.
• The planning module: Think of this as the brain. This module allows the agent to make decisions, analyse situations, and plan ahead.
• The action module: This uses all of the information from other modules to put plans and decisions into action. This is the agent’s connection to the real world.

As you can imagine, altering this architecture can dramatically change how an agent functions in the real world. Let’s elaborate by taking a look at some of the different variations of AI agent architecture. We’ll then dive deeper into how this architecture forms the basis of the eight core types of intelligent agent.

Broadly speaking, there are six core classifications of AI agent architecture.

• Reactive architectures: This is the simplest form of architecture because it doesn’t have any understanding of the world around it. It simply responds to stimuli or changes in the environment in a predetermined way.
• Deliberative architectures: This architecture has an internal understanding of the world, allowing it to make decisions on the best action to take. As such, deliberative architectures are capable of much more advanced processes and planning.
• Hybrid architectures: This combines the strengths of reactive and deliberative architectures, allowing the intelligent agent to both make autonomous decisions and react instantly to stimuli.
• Swarm architecture: As the name suggests, this architecture mimics a swarm of organisms like ants or bees. Multiple agents work in unison to solve complex problems.
• Layered architectures: This architecture features multiple layers, with higher layers dealing with abstract concepts and lower layers handling concrete tasks and stimuli. Each layer has a different function and works together to create a multi-faceted AI agent.
• Cognitive architectures: This architecture mimics the human brain. AI agents built on this framework can learn new skills and improve over time, though this naturally requires enormous datasets.

Based on the architecture you select, you can tailor your agent's actions, reactions, and decision-making processes to suit a wide range of purposes.

AI agents come in many different forms depending on the agentic architecture that they use as a foundation. Here are eight different types you need to know about:

Simple reflex agents

These simple agents function using the ‘condition-action’ principle. They react only to their current perceptions, meaning they have no deep understanding of the world around them. This works well in some scenarios, such as a customer chatbot, but limits use cases in complex industry environments.

Model-based reflex agents

These agents have an internal model of the world around them, meaning they can perceive their environment and see things that aren’t immediately obvious. They can ‘fill the gaps’ in missing information and make autonomous decisions based on their understanding of context. This makes them far more complex and agile than simple reflex agents.

Utility-based agents

These agents use a utility function to make decisions. They can evaluate different actions based on an expected utility measure to choose the optimal approach. This model is ideal when there are multiple solutions to a problem, and the agent needs to decide on the best one, such as an autonomous car deciding on the safest and quickest route.

Goal-based agents

These powerful tools are tailored to achieve specific goals. They consider the consequences of their actions and can make decisions based on whether they can use the action to achieve its objective. This means they can navigate incredibly complex scenarios autonomously and respond to the environment through sensors.

Learning agents

These agents improve over time through reinforcement learning. This is especially important in agile industries, where a business needs to stay on the cusp of new trends. For example, a virtual assistant could continually improve its service by learning more about the customer’s requirements and wants.

Hierarchical agents

These agents have a hierarchical structure. The higher-level AI agent programs and directs lower-level agents to work toward a common goal. This structure allows businesses to break down complex multi-step processes into simpler tasks, allowing each AI agent to focus on one set of responsibilities.

Multi-agent systems (MAS)

These agents interact with other agents, working collaboratively to achieve a common goal. They typically coordinate and communicate to achieve their predefined objective. All of these agents can either be homogeneous (having the same capabilities and goals), or heterogenous (each with different capabilities and goals) depending on the business’s needs.

Explainable AI agents (XAI)

XAI agents are a fairly recent concept, but they have the potential to be revolutionary for heavily regulated industries. These agents focus on transparency, providing clear justifications for every decision they make. This development could be essential in tightly controlled sectors where trust is essential, such as the financial, legal, and healthcare industries.

Adopting AI agents in customer service offers numerous benefits, transforming how businesses interact with customers and manage their service operations.

Enhanced efficiency

AI agents can handle multiple customer interactions simultaneously, significantly reducing response times and increasing the efficiency of customer service operations. This allows businesses to handle higher volumes of inquiries without compromising on the quality of service.

Improved customer satisfaction

AI agents provide quick and accurate responses, leading to higher customer satisfaction scores. They can use data to personalise interactions, enhancing the overall customer experience. And because they learn over time, they’re geared toward continuous improvement.

24/7 availability

Unlike most human agents, AI agents are available around the clock, ensuring customer inquiries are addressed promptly, regardless of time zones or business hours. This continuous availability helps businesses meet customer expectations for self-service and improves customer loyalty.

Scalability

AI agents can easily scale to handle increased volumes of customer interactions, making them ideal for businesses looking to grow without compromising service quality. As case volume increases, AI agents can be easily adjusted to handle the additional load, ensuring consistent and reliable support.

Data-driven insights

AI agents generate valuable data on customer interactions, preferences, and behaviours. Businesses can use this data to gain insights into customer needs and trends, enabling them to make informed decisions and improve their service offerings.

Consistency and accuracy

AI agents provide consistent and accurate responses to customer inquiries, reducing the risk of errors and ensuring that customers receive reliable information. This consistency helps build trust and confidence in the brand.

AI agents offer numerous benefits, including improved productivity, reduced costs, enhanced decision-making, and a better customer experience. As McKinsey found, "more than 72% of companies surveyed are already deploying AI solutions, with a growing interest in generative AI. Given that activity, it would not be surprising to see companies begin to incorporate frontier technologies such as agents into their planning processes and future AI road maps."

By leveraging these advanced AI solutions, businesses can stay ahead of the curve and drive innovation for customer engagement. (back to top)

A pre-built AI agent is useful out of the box, but to get the most out of your investment, you’ll want to personalise your intelligent agent to suit your needs. For that, you’ll need an AI agent framework.

AI agent frameworks are platforms consisting of code, APIs, and libraries that allow for the creation and deployment of AI agents. They contain all of the features and tools needed to build personalised AI agents that align with a business’s unique needs and goals.

Here are just some of the things you can build with the right AI agent framework:

• A customer service chatbot tailored to your brand values that can provide instant responses to customer queries.
• A sales assistant that automates lead generation and qualifies prospects based on your own criteria.
• Personalised recommendation engines that analyse user preferences to recommend specific products or services.
• A marketing agent that constantly analyses campaign performance against KPIs to make actionable recommendations for improvement.
• A healthcare agent that provides patient summaries to support doctors diagnoses.
• A finance agent that detects fraud autonomously and provides insights to support risk assessments.

There’s an understandable misconception that deploying custom AI agents requires high levels of coding expertise and technical knowledge, but this isn’t necessarily the case.

For instance, Agent Builder, our very own AI agent framework, allows you to build and customise AI agents with little to no coding experience. Using natural language instructions, you can develop an agent for any industry, role, and use case—regardless of your technical expertise. In essence, if you can describe an action, you can design an agent to execute it.

AI agents have the power to be revolutionary, but this doesn’t mean they’re without their risks. Anything involving AI requires careful attention to ensure it can be used safely and ethically. Here are three key concerns your business needs to consider before implementing an AI platform.

Ethical implications

For AI agents to be fair, they must be trained on unbiased data. If the data is discriminatory or unbalanced in any way, it could lead to bias, unfairness, or wrong decisions.

For instance, imagine if an AI system filtering through university applications automatically prioritised some students because they come from an affluent neighbourhood with lower crime rates. You can see why AI bias is a serious concern. Businesses need specific processes to ensure all AI agents are trained using just and fair data.

Data privacy and security

Building and training AI agents require an enormous amount of data. Businesses need robust processes for managing, storing, and processing this data to protect customers' best interests.

In addition to implementing strict data governance policies to safeguard sensitive information, businesses should be wholly transparent about collecting and storing data to build customer trust.

Technical limitations

AI agents often need time to reach their full potential. While it’s nice to think they can always grow in the right direction, this isn’t necessarily the case. What if the AI system learns the wrong ideas? This could lead to misleading decisions, unintended consequences, and unmitigated bias.

It’s important to realise that AI agents aren’t a ‘set and forget’ technology. While their value proposition is that they can act and learn autonomously, they still require agent programming from trained professionals to ensure they reach the optimal outcomes.

There's no limit to the number of use cases for AI agents. Let's briefly discuss some examples of AI agents in different industries to show you just how versatile the agent function can be.

• Healthcare: AI agents can help doctors diagnose diseases by analysing medical images. They can also build personalised patient treatment plans and assist with records management.
• Finance: Custom AI agents can help banking agencies detect fraud and provide customers with personalised finance recommendations.
• Retail: Retail businesses can automate customer service touchpoints through agent programming. AI-powered product recommendations also drive personalised shopping experiences and increase company sales.
• Manufacturing: AI agents can monitor machinery to predict maintenance requirements and optimise production processes. This boosts productivity and helps avoid expensive downtime.
• Transportation: AI agents can control autonomous vehicles, analyse traffic, avoid safety risks, and more. While this is still a budding sector, it may revolutionise how we travel.
• Energy: Software agents can help to conserve resources by identifying opportunities for energy optimisation. They can even manage renewable energy sources like solar and wind to boost production.
• Computer science: An AI code assistant can support code generation and help to find and fix errors faster. This saves programmers valuable time that they can spend in other areas of software development.
• Marketing: AI marketing agents can predict customer behaviour and determine the optimal channels, helping businesses create more effective, tailored marketing campaigns.
• Data analysis: Data science experts can use AI agents to wrangle and classify data for analysis automatically. This gives them more time to focus on drawing insights from the data they possess.

We’re only scratching the surface here. AI agents can benefit every industry in the world, from personal assistants to assembly lines and everything in between.

To show you how organisations are already beginning to build AI agents for their own benefit, let’s take a look at a real-life case study.

Manufacturing is a battlefield of innovation. Discovering new ways to optimise processes, improve workflows, and cut production costs is essential. Artificial intelligence (AI) is the natural next step for a business looking to improve efficiency.

Indian startup Control One unveiled its physical agent in 2024, and it’s already proven its worth. The technology can integrate into everyday machinery, allowing them to act autonomously in complex citations.

For instance, slow-moving vehicles can navigate and complete tasks without human intervention by integrating the AI agent system with forklifts and pallet movers. The agents can think for themselves, prioritise important work, and respond to the environment around them in real-time. As the agents' actions improve with experience, they can learn on the job with minimal intervention.

Control One’s vision-to-action agent handles all repetitive day-to-day tasks without intervention, boosting workplace safety while allowing workers to focus on other essential processes.

If you’re getting ready to deploy AI agents, here are some best practices to keep in mind:

1. Define clear objectives

Start by defining what you want to achieve with AI agents. Whether it's reducing response times, enhancing customer satisfaction, or cutting operational costs, having clear objectives will guide your implementation process and help you measure success.

2. Assess and prepare your data

AI agents rely on high-quality data to function effectively. Ensure that you have robust data collection and management systems in place. This includes customer interaction data, transaction histories, and other relevant information. Clean and structured data will enable your AI agents to provide accurate and relevant responses.

3. Choose the right AI agent type

Select the type of AI agent that best fits your needs. For instance, a reactive agent might suffice if you need an agent to handle routine customer queries. Consider a goal-oriented or learning agent that can adapt to changing customer needs and provide more sophisticated support for more complex tasks.

4. Integrate with existing systems

Ensure your AI agents integrate seamlessly with your existing CRM and customer service tools. This integration will enable a smooth flow of information and enhance the capabilities of your AI agents, allowing them to access relevant data and provide more effective support.

5. Focus on user experience

Design your AI agents with the end user in mind. Ensure that interactions are intuitive and responses are timely and accurate, providing a positive customer experience. Test your AI agents thoroughly to identify and address potential issues before deployment, ensuring they meet customer expectations.

6. Monitor and optimise

Regularly monitor your AI agents' performance elements and gather user feedback. Use this information to continuously improve your AI agents, ensuring they remain effective and relevant. This ongoing optimisation will help you adapt to changing customer needs and improve the overall performance of your AI agents.

7. Plan for human oversight

While AI agents can handle many tasks autonomously, it is important to plan for human intervention when necessary. Ensure clear guidelines for when and how human agents can assist, providing a safety net for more complex or sensitive interactions.

8. Ensure data privacy and security

Implement robust data privacy and security measures to protect customer information handled by your AI agents. This includes compliance with data protection regulations and regular security audits to safeguard sensitive data and maintain customer trust. (back to top)

Adopting AI agents will represent a significant turning point in the business landscape. Previously, automating tasks relied entirely on predefined input from human users. Now, artificial intelligence can perform tasks and learn with minimal intervention. It's an exciting time for business owners.

It is easy to predict that AI agents will become more advanced in the future. As machine learning, large language models (LLMs), and natural language processing (NLP) tools develop, so too will their ability to learn, improve, and make more informed decisions.

How will this impact the workplace? The benefits of agent functionality are obvious. We can expect faster decision-making, more productivity, and more space for experts to focus on high-value processes. On the other hand, introducing autonomous agent models at scale is daunting for everyone involved.

To ensure the transition goes smoothly, businesses need to proactively implement processes to ensure AI can be managed ethically and responsibly. Providing staff training, keeping up to date with compliance, and implementing robust data engineering and governance protocols will ensure the AI revolution is something to be welcomed, not feared.

If you're looking to integrate AI agents into your customer service strategy, Salesforce Service Cloud can help. Here’s how:

Intelligent AI agents
With Agentforce, the always-available agentic layer of the Salesforce platform, you can leverage custom AI agents to automate core tasks, gain valuable insights, and make informed decisions. Engage customers autonomously across channels 24/7 with human-like interactions and resolve cases swiftly by grounding every response in trusted data.

Integrated platform with Data Cloud
Salesforce Service Cloud provides an integrated platform that combines customer service AI, CRM, and intelligent automation tools. This integration ensures that Agentforce has access to comprehensive and up-to-date customer data, enabling it to deliver more accurate and personalised service.

The inclusion of Data Cloud allows for seamless data integration and management, providing a unified view of customer data across all channels.

Scalability with the Salesforce Platform

Service Cloud is built on the Salesforce Platform, which brings trust, security, and scalability to your customer service operations. This next-gen platform allows you to scale your AI solutions, handling increasing volumes of customer interactions without compromising performance.

Customisation with advanced tools
Service Cloud offers extensive customisation options to tailor your AI agents to your specific business needs. Tools like our Prompt Builder, powered by Agentforce, enable you to create customised workflows and responses, ensuring that your AI agents align with your brand and customer service goals.

With the flexibility of Apex Code and the integration capabilities of MuleSoft, you can connect Service Cloud with other systems and extend its functionality to meet your requirements.

Seamless integration and interoperability
Service Cloud’s open architecture ensures seamless integration with other systems and platforms, enabling you to build a cohesive and efficient customer service ecosystem. Whether you need to integrate with existing CRM systems or third-party applications, Service Cloud offers the flexibility and interoperability required to create a seamless customer experience.

AI agents represent a great leap forward for service organisations by providing personalised support at scale. Businesses that embrace this technology will be well-positioned to reduce costs while meeting the demands of modern customers in a competitive global market. (back to top)