What Are AI Agents?

Wouldn’t it be great if everyone in your company — from the CEO to the newest sales rep — had an assistant? Someone always ready to help, who knows your customers well and who can offer detailed advice about what to do next. With various types of AI agents, that possibility is here — and growing.

AI agents are a type of artificial intelligence (AI) system that can understand and respond to customer enquiries without human intervention. They are created using an agent builder, like Agentforce and rely on machine learning and natural language processing (NLP) to handle a wide range of tasks. These intelligent agents can include anything from answering simple questions to resolving complex issues — even multi-tasking. Most importantly, they 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.

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.

It can be hard to deliver the kind of personalised customer service that financial customers now expect — but an agentic assistant eases this pain. Drawing from unified customer data, an agentic AI can surface relevant insights for your human agents, tailoring financial recommendations to each customer’s needs and goals. AI can also help you better prepare for client meetings.

Accurately summarising client support interactions takes detailed review and can be prone to human error. An AI agent helps with this, automatically summarising open cases, open orders, invoices and recent activity — saving your staff time and money.

Intelligent systems can monitor machinery to predict maintenance requirements and optimise production processes. This boosts productivity and helps avoid expensive downtime. AI agents can also help your sales team move deals down the pipeline.

With AI, you can summarise sales agreements to highlight deviations in planned versus actual quantities and revenue, helping you to make better, more informed decisions.

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.

Automotive companies can use AI agents for a complete view of vehicle or fleet performance. They can surface the most critical or time-sensitive vehicle alerts based on vehicle telematics. With AI, you can resolve issues proactively with quick visibility into maintenance needs and choose from recommended actions.

Dealerships and repair shops can use agentic AI for commerce, quickly and easily creating promotions that appeal to their target market.

Intelligent systems can deliver next-level customer experiences in healthcare, too. A patient services agent not only answers questions, but helps patients schedule the best doctor for their needs. The system can review coverage benefits, generate medical history summaries and approve care requests.

They can also build personalised patient treatment plans and assist with records management. Looking for ideal candidates for trials? An AI agent can match eligible candidates to relevant clinical trials using patient details and study criteria, simplifying analysis.

You can also use agentic systems to gain a complete view of your provider network. This allows you to quickly review provider information and past performance, boosting efficiency and lowering patient wait times.

While AI agents can help a variety of industries, they're not all the same. Here’s a look at a few distinct types that you can use to help your business.

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.

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 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.