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.

Here’s a breakdown of how they operate:

• Perception and data collection: Agentic systems 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 agentic AI can integrate and process data in real time, providing them with the most up to date information to handle enquiries effectively.
• Decision making: Using sophisticated machine 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. This decision-making process is enhanced by the agent's ability to learn from previous experiences and refine its responses over time.
• Action execution: Once a decision is made, digital assistants can execute the required action. 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: 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 they remain effective and relevant, even as customer expectations and business environments change.

By combining these capabilities, intelligent systems can handle a wide range of 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.

The adoption of AI agents offers numerous benefits, transforming how businesses interact with their customers and manage their service operations.

1. Increased efficiency: This technology can handle multiple customer interactions simultaneously, significantly reducing response times and increasing the efficiency of customer service operations. They are also able to identify if they should escalate to a human, then select the representative with the best skill set to take the enquiry. This allows businesses to handle higher volumes of enquiries without compromising on the quality of service.
2. Improved customer satisfaction: Autonomous 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.
3. Improved decision making: The ability to analyse vast datasets enables these autonomous systems to discern subtle patterns and emerging trends, delivering valuable insights that empower organisations to make more accurate and timely decisions.
4. 24/7 availability: These systems are available around the clock, ensuring that customer enquiries 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.
5. Scalability and adaptability: AI 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.
6. Data-driven insights: AI generates 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.
7. Accuracy and consistency: Agentic AI provide consistent and accurate responses to customer enquiries, reducing the risk of errors and ensuring that customers receive reliable information. They can improve the accuracy of your responses through agentic loops and human-like reasoning. This consistency helps build trust and confidence in the brand, as customers get the experience they’ve come to expect.
8. Cost savings: Automating routine tasks with digital labour generates significant cost savings by handling the operational burden, thus allowing human employees to dedicate their time and expertise to more complex problem-solving and strategic initiatives. This shift in focus ultimately boosts productivity and profitability.

AI agents offer numerous benefits, including improved productivity, reduced costs, enhanced decision-making and a better customer experience. As management consulting firm 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 innovate for customer engagement.

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 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)

Companies in several different industries are seeing the benefits of integrating agentic systems. Let’s dig into some AI agent examples by industry, with specific use cases, that show how versatile this technology can be.

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.

Open Universities Australia (OUA) is dedicated to delivering accessible education to Australians. But accessibility isn’t always easy when you’re appealing to a mass audience. OUA realised the ability of AI to create more personalised experiences for its students.

OUA partnered with LivePerson to create an agentic tool that can engage with new students, handle inquiries with empathy, and provide warm, humanised conversation to help ease anxieties. Here’s what they achieved:

• Student engagement: OUA designed its agentic system to mirror the talking points and humanisation of the brand’s student advisors. As the AI agent can think autonomously, it can answer questions and provide thoughtful responses to excite, engage, and empathise with students experiencing a wide range of emotions when starting their time at university.
• Conversion rates: The agent system gathers information and provides custom summaries to student advisors. This gives educators more detail about students, which they can use to tailor interactions and provide personalised experiences.
• Efficiency: The AI tool handles all early questions and concerns, freeing up educator' time to provide more frequent, high-quality support to their students.

Despite the benefits of this conversational AI solution, the OUA had understandable concerns about AI bias, hallucinations, and safety. As such, they implemented several safeguards, such as ensuring all data was handled and processed within Australia and rigorously testing the agent to ensure it is safe for student use.

With those concerns alleviated, OUA integrated a chatbot that increased student satisfaction, improved lead qualification and conversion rates, and freed up educator time to focus on higher-value tasks.

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)

With an agentic worker, you can improve inventory management. It can highlight the expected versus actual inventory checked out at the end of every tour. You can contextualise these assessments with added detail, like whether or not they were counted on truck or a part of the original load document.

It also makes managing your marketing campaigns simpler and can generate promotional content to keep people in the loop on new products.

Utility-based agents use an 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.

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 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.

Here, a higher-level AI agent programmes 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.

Chatbots and AI agents have different jobs. Chatbots are usually designed for one specific task, like customer service or finding information. They follow rules and scripts and they use pattern matching and keyword recognition to respond. This makes them good at handling simple questions, but they can't understand complex contexts or adapt to new situations.

AI agents are more advanced and independent. They can handle a wider range of tasks, learn from interactions and get better over time. Autonomous agents can understand and keep context across multiple conversations, which makes them good for more complex and dynamic environments. They can also integrate with different systems and platforms, so they can do tasks that need a deeper understanding of the user's needs and the environment.

For example, AI agent use cases include managing a user's calendar, making bookings and giving personalised recommendations, while a chatbot might only be able to answer FAQs or process simple transactions.

The distinction between AI agents versus chatbots is becoming increasingly blurred. However, AI agents often have more capabilities and autonomy than traditional chatbots, making them the future of human-AI collaboration.

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

1. Define clear objectives. Start by defining what you want to achieve with your intelligent agents. 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.
3. Choose the right AI agent type. Select the type that best fits your needs.
4. Integrate with existing systems. Ensure that your AI systems are seamlessly integrated with your existing CRM, communication and collaboration tool and other customer service tools.
5. Focus on user experience. Design your digital workforce with the end-user in mind. Ensure that interactions are intuitive and responses are timely and accurate, providing a positive customer experience. customer expectations.
6. Monitor and optimise. Regularly monitor the performance of your AI solutions and gather feedback from users. Use this information to make continuous improvements to your AI agents, ensuring that they remain effective and relevant.
7. Plan for human oversight. While AI agents can handle many tasks autonomously, it's important to have a plan for human intervention when necessary.
8. Ensure data privacy and security. Implement robust data privacy and security measures to protect customer information.

AI agents can provide a much-needed boost for your company, across several departments. From providing personalised customer support to generating and deploying promotions tailored to your target market, here’s how this technology can help your teams accomplish more.

With AI agents in place, your customer service team can resolve customer enquiries in their sleep — literally. AI responds to your customers’ questions 24/7, escalating priority cases to your human agents, including all the necessary context. Agentforce for Service can do this autonomously across all channels, drawing from your trusted customer data and responding in your brand’s voice.

You can set your Agentforce for Service up in minutes with prebuilt templates or quickly customise agents to fit your needs.

Much like how your service team can use AI to respond to enquiries around the clock, your sales team can autonomously answer product questions at all hours and book meetings for sales reps. Agentforce Sales Development Representative (SDR) Agents respond immediately and accurately, using responses grounded in your data. You can set how often, which channels and when your Agentforce SDR engages before escalating to your employees.

Digital workers can be a huge help to your commerce team, too. AI agents offer personalised product recommendations and even give shoppers a personal assistant, drawing from your trusted customer data. With Agentforce, AI can respond to customers directly on your commerce site or on messaging apps like WhatsApp. AI can help people make purchases faster by guiding search queries and tailoring product recommendations to the shopper.

Want better, fully-optimised marketing campaigns? AI agents can help your marketing team build better campaigns — faster. With Agentforce Campaigns, autonomous assistants generate a campaign brief and target audience segment, then create relevant content speaking to those audiences. AI can even build a customer journey in Flow. AI agents also continually analyse campaign performance against your key performance indicators and proactively recommend improvements.

Think of AI agents as the always-on help for all your teams. They allow your employees to get more done, giving customers the personalisation they’ve come to expect.

It's an exciting time for business owners. The adoption of AI agents represents a significant turning point. Automating tasks used to rely on predefined input from human users, but now, AI agents can perform tasks and learn with minimal intervention.

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.

We can expect faster decision-making, more productivity and more space for experts to focus on high-value processes.

With all these new AI developments, introducing autonomous agent models at scale can seem like a daunting task. That’s why we created Agentforce, the fastest and easiest way to build AI agents. And you don’t have to be an IT professional to build them. Simply describe what you need it to do, using natural language and Agentforce does the rest. Give it a try today.