What are Autonomous Agents? A Complete Guide

Businesses are starting to realize the power of artificial intelligence (AI) by using AI agents, which help companies become more efficient and improve customer interactions. But autonomous agents take that a step further. Autonomous AI agents are able to continually improve their own performance through self-learning, and without the need for human intervention.

What does this mean for your business? Using autonomous AI, your company can have a fleet of superpowered agents tailored to each department’s needs. Autonomous AI agents can instantly draw from your trusted customer data, delivering the most up-to-date and accurate information to your employees and customers.

Here’s a complete breakdown of what autonomous AI agents are, how they work, and how they can help different industries and teams make the most of artificial intelligence.

What we'll cover:

• What is an autonomous agent?
• How do autonomous agents work?
• What is the difference between autonomous agents and AI agents?
• Examples of autonomous agents by industry
• Types of autonomous agents
• Examples of how autonomous agents can help your teams
• How to include autonomous agents in your strategy
• How autonomous agents can help your business

An autonomous agent is an advanced form of AI that can understand and respond to inquiries, then take action without human intervention. When they’re given an objective, they can generate tasks for themselves, complete assigned tasks, and work on the next one, until the objective is complete.

Unlike traditional software programs that follow predefined rules, autonomous agents can operate in dynamic environments, making them ideal for complex tasks in customer service, marketing, commerce, sales, and more.

While autonomous AI agents don’t need human help to complete their task, they still need us to provide prompts that establish goals and the main objective to complete.

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Autonomous agents function through a combination of advanced technologies, including machine learning, natural language processing (NLP), and real-time data analysis. Here's a closer look at how they work:

• Perception and data collection. Autonomous agents start by gathering data from various sources, such as customer interactions, transaction histories, and external databases. This data collection is crucial for understanding the context of each task and making informed decisions.
• Decision-making. Using machine learning algorithms, autonomous agents analyze the collected data to identify patterns and predict outcomes. They use this information to make decisions that align with their goals. For example, an autonomous agent in customer service might analyze past interactions to determine the best way to respond to a customer's query.
• Action execution. After making a decision, the agent executes the necessary actions to achieve the desired outcome. This could involve answering customer questions, processing orders, or escalating complex issues to human agents. The execution process is designed to be efficient and seamless, ensuring a smooth customer experience.
• Learning and adaptation. One of the key features of autonomous agents is their ability to learn from each interaction. They continuously update their knowledge base and refine their decision-making algorithms to improve performance over time. They refine their decision-making capabilities using reinforcement learning, which improves performance over time. This adaptability allows them to handle an ever-increasing range of tasks and scenarios.

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While all autonomous agents are technically AI agents, not all AI agents are autonomous. AI agents include assistive AI agents, like copilots, which use human intervention to complete many tasks. While both assistive and autonomous AI agents are able to learn and make decisions based on new information, only autonomous agents can complete several tasks in a row. AI agents are more like an essential tool to help human workers focus on what matters most, while autonomous agents can execute on tasks and workflows independently.

AI agents are designed to work with a human involved, while autonomous agents are built to be self-sufficient with little to no human intervention.

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While all autonomous agents are technically AI agents, not all AI agents are autonomous. AI agents include assistive AI agents, like copilots, which use human intervention to complete many tasks. While both assistive and autonomous AI agents are able to learn and make decisions based on new information, only autonomous agents can complete several tasks in a row. AI agents are more like an essential tool to help human workers focus on what matters most, while autonomous agents can execute on tasks and workflows independently.

AI agents are designed to work with a human involved, while autonomous agents are built to be self-sufficient with little to no human intervention.

With this technology, banks can use Agentforce to autonomously manage a transaction dispute through the various channels like the banking app, SMS, website or call. Agentforce for Banking uses the prebuilt services flows from Financial Services Cloud to file the complaint to meet regulatory reporting requirements, verify the customers’ transaction history and spending patterns, alert the merchant, and even issue a provisional credit or new card.

These autonomous agents only escalate to a human agent to review and grant the proper authorizations, saving them time to focus on more complex customer interactions.

Autonomous agents in insurance can update coverage and extend better pricing and or coverage to qualified policyholders; update beneficiaries, schedule & deploy claims adjusters, even issue claims checks or policy renewals — all without human intervention.

Wealth advisors reported that 67% of their daily work is non-value-added administrative work. Agentforce can reduce the employee time spent on these kinds of tasks, so they can focus on more important issues. Agentforce can plan, schedule, and summarize client meetings, then draft client communications for follow-up meetings.

Agentforce autonomous agents will research the discussion points, like bond performance, explore exposure to international companies, or even check account credentials for power of attorney status to ensure proper compliance. They will even route any communications to the proper licensed supervisors to ensure full compliance and oversight.

By using autonomous agents in healthcare, you can improve the patient experience. An autonomous AI agent can engage with patients, providers, and payers to resolve inquiries, provide summaries, and take action. For instance, a patient services agent can answer simple patient questions and help schedule appointments with the best physician for their needs. The autonomous agent can review coverage benefits, generate medical history summaries, and approve care requests.

For retail companies, autonomous agents can do a lot without human intervention. They can share campaign insights, proactively manage customer outreach, and resolve cases.

A personal shopper autonomous agent is like a digital concierge for online shoppers, using generative AI to help customers on ecommerce sites, chat, or messaging apps like WhatsApp. While basic chatbots are only able to solve predefined questions, autonomous AI agents can learn from shoppers’ behavior and preferences. This allows people to use natural language when they search, get conversational responses, and quickly add items to their cart for instant checkout.

An autonomous communications agent provides faster customer support around billing inquiries. For instance, if a customer submits a complaint about a charge on their account, the autonomous agent analyzes past monthly bills, identifies the issue, and validates the dispute request. This helps customers get the support they want quickly, freeing up human agents to work on higher-priority cases.

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Not all autonomous agents are alike. There are many kinds of autonomous AI agents, all who work in different ways to achieve their objectives. Here are a few of the most common types of autonomous agents you have to pick from.

• Reactive autonomous agents: These autonomous AI agents are the simplest kind. They make decisions based on their specific inputs and predefined actions, immediately reacting to new data, but not retaining it or learning from past interactions.
• Deliberative autonomous agents: Also known as cognitive or reasoning agents, deliberative autonomous agents make smart decisions by analyzing the environment and aligning with the user’s objectives. They model the world, weigh different actions, and select the best path to achieve your goals.
• Hybrid autonomous agents: Hybrid autonomous agents combine elements of reactive and deliberative agents. These dynamic helpers can swiftly react to immediate changes in their environment while also staying focused on the long-term objective.
• Model-based autonomous agents: These autonomous agents can be expertly programmed or powered by machine learning to not only make decisions, but learn as they go. When faced with an incomplete data set, they'll fill in the blanks with smart predictions based on their past experiences.
• Goal-based autonomous agents: Goal-based agents make smart decisions by evaluating how likely a specific action will help achieve your goals. They're adaptable and can make internal adjustments on the fly, responding to changes in the environment or shifts in your objectives.
• Utility-based autonomous agents: These agents evaluate actions using a smart utility function that ranks outcomes based on how well they meet your goals. In real time, utility-based autonomous agents can quickly optimize their performance based on the criteria you’ve set.

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There’s a lot that autonomous agents can do, and their utility isn’t limited to one team or department. Let’s see how autonomous AI agents can provide a boost across your company.

We’re just beginning to understand the tremendous potential of autonomous agents in customer service. But here are some notable applications:

• Personalized interactions: Our research shows that 65% of customers expect companies to adapt to their changing needs and preferences. By analyzing customer data, autonomous agents can offer personalized recommendations and solutions to enhance the customer experience. For example, they can suggest products based on past purchases or provide tailored advice based on a customer's preferences.
• Proactive service: Autonomous agents can anticipate customer needs and provide proactive support, such as sending reminders for upcoming appointments or notifying customers about potential issues before they arise. This proactive approach helps prevent problems and increases customer loyalty.
• Multi-channel support: Autonomous agents can manage customer interactions across multiple channels, including email, chat, social media, and phone. This ensures consistent and efficient service, regardless of how customers choose to contact the company.

Autonomous agents can help your sales team gain leads and move them down the pipeline, around the clock. Sales Development Representative (SDR) agents, like the ones in Agentforce, autonomously answer customers’ product questions, handle objections, and book meetings for your sales reps.

Drawing from your trusted customer data, autonomous agents respond to inquiries and take action accurately. You can decide how often, on which channels, and when your autonomous AI agents will engage before they escalate the case to your human agents to close the deal. Autonomous agents can meet leads on their preferred channels, like SMS or WhatsApp, and in any language.

You don’t need to have a staff of personal shoppers guiding customers to the right product — autonomous agents can do this. With autonomous AI agents in place, you’re giving shoppers their own personal assistant, no matter what time it is or where they are.

Autonomous agents can draw from customers’ purchase history and preferences for tailored product recommendations, helping people make purchases faster. With Agentforce, autonomous agents can also respond to people through your commerce site or on a channel they prefer, like WhatsApp.

Commerce teams can also use autonomous AI agents to create and deploy targeted, relevant promotions.

When your marketing team uses autonomous agents, they can easily create, manage, and optimize campaigns from end-to-end. This technology helps marketers save time by generating a campaign brief, target audience segment, and content. Autonomous agents can even build a customer journey in Flow, Salesforce’s automation tool.

Autonomous agents can analyze their performance against the key performance indicators you set while continuously and proactively recommending improvements.

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Deploying autonomous agents requires careful planning and execution. Here are some best practices to ensure successful implementation:

• Define clear objectives: Start by outlining what you want to achieve with autonomous agents. Whether it's improving response times, enhancing customer satisfaction, or reducing operational costs, having clear objectives guides your implementation process and helps you measure success.
• Assess your data infrastructure: Autonomous 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 helps your agents provide accurate and relevant responses.
• Select the right technology: Choose autonomous agent technologies that align with your business needs and objectives. Consider factors such as scalability, integration capabilities, and ease of use. Evaluate different vendors and solutions to find the best fit for your organization.
• Integrate with existing systems: Ensure that your autonomous agents are seamlessly integrated with your existing customer relationship management (CRM) software and other tools. This integration enables a smooth flow of information and enhances the capabilities of your agents to allow them to access relevant data and provide more effective support.
• Focus on user experience: Design your autonomous agents with the end user in mind. Ensure that interactions are intuitive and that the agents can provide timely and accurate responses to ensure a positive customer experience. Test your agents thoroughly to identify and address any potential issues before deployment, ensuring that they meet customer expectations.
• Monitor and optimize: Regularly monitor the performance of your autonomous agents and gather feedback from users. Use this information to make continuous improvements to your agents, ensuring they remain effective and relevant. This ongoing optimization helps you adapt to changing customer needs and improves the overall performance of your agents.
• Plan for human oversight: While autonomous agents can handle many tasks independently, it's important to have a plan for human intervention when necessary. Create clear guidelines for when and how human agents should step in to assist, providing a safety net for more complex or sensitive interactions.
• Ensure data privacy and security: Implement robust data privacy and security measures to protect customer information handled by your autonomous agents. This includes compliance with data protection regulations and regular security audits to safeguard sensitive data and maintain customer trust.

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If you're looking to integrate autonomous agents into your business, Agentforce can help. Here’s how:

• Intelligent AI agents: Engage customers across channels 24/7 with human-like interactions and resolve cases swiftly by grounding every response in trusted data.
• Integrated platform with Data Cloud: Agentforce provides an integrated platform that combines AI, CRM, and customer automation software. This integration ensures that your autonomous agents have access to comprehensive and up-to-date customer data, so they can deliver more accurate and personalized service. Including Data Cloud allows for seamless data integration and management, providing a unified view of customer data across all channels.
• Scalability: Agentforce brings the trust, security, and scalability of Salesforce to all of your teams. This platform allows you to scale your AI solutions, handling increasing volumes of actions without compromising performance.
• Customization with advanced tools: Agentforce offers extensive customization options to tailor your autonomous agents to your specific business needs. Tools like Prompt Builder and Copilot Studio help you create customized workflows and responses, so that your autonomous agents align with your brand and customer service goals. With the flexibility of Apex Code and integration capabilities of MuleSoft, you can connect Agentforce with other systems and extend its functionality to meet your requirements.
• Seamless integration and interoperability: Agentforce can seamlessly integrate with external systems and platforms, enabling you to build apps and workflows grounded in all of your business data, no matter the source or data type. Whether you need to integrate with existing CRM systems or third-party applications, Agentforce offers the flexibility and interoperability required to create a seamless customer experience.

No matter the team or industry, autonomous agents can help your company become more efficient. By delegating tedious, time-consuming tasks to autonomous AI agents, your employees can focus on what matters most: your customers.

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