What Are AI Agents?

By Magulan Duraipandian, Sr. AI Solutions Technical Evangelist - Salesforce

AI agents transform the way companies operate and interact with their customers. These intelligent systems are designed to automate complex tasks, provide personalized experiences, and free up human workers to tackle more demanding challenges.

AI agents are a type of artificial intelligence (AI) system that can understand and respond to customer inquiries 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.

• AI agents are more advanced and autonomous than chatbots, capable of learning and handling a wider range of complex tasks.
• They offer significant business benefits, including increased efficiency, cost savings, and improved customer satisfaction.
• AI agents have versatile applications and are built for specialized functions across various industries, such as sales, marketing, and finance.

• The concept of AI agents has been a subject of research for many years. While the idea of autonomous, intelligent systems has been around for decades, recent advancements in large language models (LLMs) have provided a "missing component", making current capabilities more accessible and powerful. These advancements have allowed AI agents to move beyond theoretical discussions into practical, real-world applications.

AI agents operate through a process that mirrors human thought, allowing them to interact and solve problems autonomously. They begin by gathering data, then processing that information to make a decision, which they then act on. This entire cycle is continuously refined through learning and adaptation.

• 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 inquiries effectively.
• Decision making: Using sophisticated machine learning models, AI agents analyze 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. 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 allows humans to focus on complex tasks that add value.

Understanding the internal workings of AI agents requires a look at their fundamental building blocks. These components enable agents to perceive, reason, and act effectively:

• Underlying Model (LLM): Large Language Models (LLMs) serve as the brain of many modern AI agents, providing the foundational natural language understanding and generation capabilities.
• Memory: AI agents require various forms of memory to maintain context and learn over time. This can include:
  • Short-term memory: For immediate conversational context.
  • Long-term memory: For retaining knowledge and learning from past experiences.
  • Episodic memory: For remembering specific interactions or events.
  • Consensus memory: For shared knowledge within multi-agent systems.
  • Mechanisms like "chunking and chaining" are often used to manage and retrieve information efficiently from memory.
• Persona: This component helps an AI agent maintain a consistent character, tone, and style throughout interactions, contributing to a more natural and predictable user experience.
• Tools: To perform actions in the real world, AI agents need access to various external tools, APIs, and programs. These tools allow agents to retrieve information, execute commands, or interact with other systems.
• Agent Action: The mechanism by which the AI Agent performs tasks and interacts with its environment. Actions can include calling external functions, invoking APIs, or controlling physical actuators.
• Architecture: This refers to the overall structural design of the AI agent, encompassing its physical or software base. It defines how the different components interact and how data flows within the system.

Beyond the general operational loop, AI agents employ specific reasoning paradigms to handle complex, multistep problems:

• ReAct (Reasoning and Action): This paradigm allows an agent to interleave reasoning (e.g., internal monologue, planning) with actions (e.g., using tools, querying databases). This enables more dynamic and adaptable problem-solving.
• ReWOO (Reasoning WithOut Observation): In this approach, the agent performs its reasoning process without constantly observing the environment after each step, which can be more efficient for certain tasks.

These distinct architectural approaches allow agents to handle nuanced and complex scenarios more effectively than simpler systems.

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, on the other hand, are more advanced and independent. They can handle a wider range of tasks, learn from interactions, and improve over time. Autonomous agents can understand and keep context across multiple conversations, making them suitable for more complex and dynamic environments. They can also integrate with different systems and platforms, performing tasks that require a deeper understanding of user needs and the environment.

For example, AI agent use cases include managing a user's calendar or making personalized recommendations, while a chatbot might only answer FAQs. The distinction is blurring, but AI agents generally possess more capabilities and autonomy.

While often used interchangeably, there's a nuanced difference. AI assistants, like Microsoft 365 Copilot, often work alongside users to augment their capabilities. AI agents can be seen as a step further, possessing higher levels of autonomy and the ability to proactively take actions to achieve goals, sometimes working in collaboration with or independently of human intervention. The key distinctions often lie in purpose, capabilities, interaction, autonomy, complexity, and learning.

AI agents offer many exciting advantages for businesses in just about any industry.

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 inquiry. This allows businesses to handle higher volumes of inquiries 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 personalize 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 analyze vast datasets enables these autonomous systems to discern subtle patterns and emerging trends, delivering valuable insights that help organizations to make more accurate and timely decisions.
4. 24/7 availability: These systems are available around the clock, ensuring that 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.
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 behaviors. 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 inquiries, 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. Although AI agents perform routine tasks with remarkable consistency, human oversight and validation are vital for critical applications and dynamic environments to guarantee reliability.
8. Cost savings: Automating routine tasks with digital labor 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." Using these advanced AI solutions, businesses can stay ahead of the curve and innovate for customer engagement.

By leveraging these advanced AI solutions, businesses can stay ahead of the curve and innovate for customer engagement.

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 optimize. 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 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.

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.

1. Sales development representative (SDR) agents: These agents automate parts of the sales process to help with lead qualification, coaching sales teams, and identifying upsell opportunities. They act as a digital team member, working 24/7 to nurture leads and book meetings, freeing up human reps to focus on building relationships and closing deals.
2. Customer service agents: These AI assistants help to answer customer questions and resolve problems with minimal human intervention. They can handle a large volume of requests across various channels and can help human agents by summarizing past interactions and suggesting responses.
3. Marketing campaign agents: These agents are used to support marketing teams in creating end-to-end campaigns. They can generate campaign briefs, design customer journeys, and create content to help marketers save time and effort.
4. Ecommerce agents: These agents assist with various tasks related to online sales to help enhance the user experience and boost operational efficiency. They can provide personalized product recommendations and help guide customers through the purchasing process.
5. Collaboration agents: These agents are specialized AI systems that work together to help teams be more effective. They can help break down silos by sharing data and insights across different departments, creating a more unified workflow.

AI agents can provide a much-needed boost for your company, across several industries and departments, by offering deeper levels of automation, personalization, and insight. Here’s how this technology can help your teams accomplish more:

• Personalized client service: Delivering the kind of personalized customer service that financial customers now expect can be challenging — but AI agent eases the pain. Drawing from unified customer data, an agentic AI can surface relevant insights for humans, tailoring financial recommendations to each customer’s unique needs and goals. For example, an agent could analyze a client's spending habits, investment portfolio, and risk tolerance to suggest specific savings plans or investment products.
• Enhanced meeting preparation: Accurately summarizing client support interactions takes detailed review and can be prone to human error. An AI agent aids in this process by automatically condensing ongoing cases, open orders, invoices, and recent actions, resulting in saved time and money for your team. This allows financial advisors to walk into meetings fully prepared with a comprehensive client overview.

• Predictive maintenance and production optimization: Intelligent systems can monitor machinery to predict maintenance requirements and optimize production processes. This boosts productivity and helps avoid expensive downtime. An AI agent could analyze sensor data from machines to identify abnormal patterns indicating potential failure, then automatically schedule maintenance or order replacement parts.
• Sales agreement analysis: AI agents can help your sales team move deals down the pipeline. With AI, you can summarize sales agreements to highlight deviations in planned versus actual quantities and revenue, helping you make better, more informed decisions on production runs or inventory.

• Optimized inventory management: With an agentic worker, you can improve inventory management. AI can highlight the expected versus actual inventory checked out at the end of every tour. You can contextualize these assessments with added detail, like whether or not they were counted on truck or a part of the original load document. This allows for more precise stock management and reduced waste.
• Automated promotional content generation: AI agents make managing your marketing campaigns simpler by generating promotional content tailored to keep people in the loop on new products or seasonal offers. An agent could analyze customer purchase history and preferences to create personalized email campaigns or ad copy.

• Vehicle performance monitoring and proactive maintenance: 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 telemetry. With AI, you can resolve issues proactively with quick visibility into maintenance needs, and choose from recommended actions, such as dispatching a mobile service unit or alerting the driver to schedule an appointment.
• Dealership commerce and promotions: Dealerships and repair shops can use agentic AI for commerce, quickly and easily creating promotions that appeal to their target market. An agent could analyze local market trends and customer demographics to suggest optimal pricing strategies or special offers for specific vehicle models.

• Personalized patient services: Intelligent systems can deliver next-level customer experiences in healthcare. A patient services agent not only answers questions, but helps patients schedule the best physician for their needs, reviewing coverage benefits, generating medical history summaries, and approving care requests.
• Clinical trial matching and provider network optimization: AI agents can build personalized patient treatment plans and assist with records management. They can also match eligible candidates to relevant clinical trials using patient details and study criteria, simplifying analysis. Furthermore, you can use agentic systems to gain a complete view of your provider network, allowing you to quickly review provider information and past performance, boosting efficiency and lowering patient wait times.

24/7 autonomous customer support: With AI agents in place, your customer service team can resolve customer inquiries in their sleep — literally. AI responds to your customers’ questions 24/7, escalating priority cases to humans, 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 customize agents to fit your needs. For instance, an agent could handle password resets, update shipping information, or provide basic troubleshooting steps, freeing humans for more complex issues.

Autonomous sales development and meeting booking: Much like how your service team can use AI to respond to inquiries 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. An agent could qualify leads, answer frequently-asked questions about products, and even schedule follow-up calls.

Personalized shopping experiences: Digital workers can be a huge help to your commerce team. AI agents offer personalized 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 based on their browsing history, past purchases, and even real-time intent.

• Automated campaign creation and optimization: Want better, fully-optimized marketing campaigns? AI agents can help your marketing team build better campaigns — faster. Autonomous agents 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 analyze campaign performance against your key performance indicators and proactively recommend improvements, such as A/B testing different headlines or adjusting ad spend based on real-time engagement data.

Think of AI agents as the always-on help for all your teams. They allow your employees to get more done, giving customers the personalization 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.

The future of AI agents is likely to involve increasingly sophisticated collaboration among agents, leading to the development of multi-agent systems and agent ecosystems. This will enable more complex tasks to be automated and new capabilities to emerge through the collective intelligence of multiple agents.

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. Learn more about AI agents and how they can help your business.