Types of AI agents: Which one is right for your business?

Think about how much of your day is spent on repetitive tasks. What if Artificial Intelligence (AI) could handle those for you? That's a possibility now. Businesses embracing AI agents are not just saving time, but also creating more space for meaningful work.

The AI agents market is expected to expand at a CAGR of 44.8% between 2024 and 2030, driving 20% to 30% productivity gains¹. Why? Because AI isn't just about automation anymore. It's about empowering you to work smarter. AI agents can now plan and take action, not just assist. Let’s look at the types of agents in AI, how they work, and where they are used. This will help you understand how to select the right AI agent for your business goals.

Understanding AI agents: Foundation of modern customer service

Support is a loaded function for a business. It has to run continuously while managing the always increasing volume of tickets. AI agents offer a way to scale support efficiently and free up your resources to focus on higher-order tasks such as strategy and creative solutions.

Businesses are already creating strong teamwork between humans and AI agents, enabling smarter work. As per a recent KPMG report, more than 51% of organizations plan to invest in AI agents by 2025. Let's dive into how AI agents are reshaping everything, from customer care and service to supply chain decisions.

How AI agents are transforming business operations

AI agents differ from chatbots and assistants. Unlike traditional chatbots, which follow strict rules and have a narrow focus, AI agents work independently. They don’t just respond; they plan, reason, execute, and improve in real time. Here are some of the common business outcomes you can derive with different types of agents in AI:

1. Faster customer response times

Speed matters in customer service, and AI agents deliver. They analyze, route, and resolve issues instantly, working 24/7 to manage high data volumes across multiple channels.

Imagine a customer reaching out with an urgent issue during peak hours. Instead of waiting in a long queue of calls, they receive the following instant response: "Thank you for reaching out, Ms. Wright. I’ve marked your issue as high priority and connected you with a technical specialist for immediate support. Less urgent queries will stay in the queue."

By handling routine interactions and prioritizing critical cases, AI agents speed up resolutions while maintaining quality service.

2. Enhanced personalization

As technology evolves, customers expect more personalized experiences. Until recently, chatbots used broad customer demographics, but now they leverage detailed customer profiles for more personalized interactions. AI-driven personalization taps into unique data from thousands of past interactions, including:

• Browsing and purchase history 

• Customer interactions 

• Behavioral and demographic data 

• Customer surveys and product ratings 

• Real-time customer engagement data

By analyzing this data, AI agents deliver hyper-personalized experiences, providing recommendations, resolving issues, and anticipating customer needs, all in real time.

3. Cost efficiency and resource optimization 

In the past, scaling customer service meant hiring more staff, a costly and time-consuming process. Businesses often had to choose between premium service at a high cost or a leaner, less personalized approach.

AI has changed this entirely. Customer experience leaders can now strengthen customer service without adding more team members. AI in customer service can now handle routine tasks like:

• Managing low-priority tickets

• Performing data entry

• Analyzing call patterns to identify trends

Case study: Total Expert saw a 248% ROI in customer service after using Freddy AI.

AI agents allow human agents to focus on complex issues where problem-solving and empathy matter most. The result? Better service, lower costs, and a more efficient operation. Let's look at different types of agents in AI with examples to discover how they’re reshaping everything.

7 different types of AI agents for business

“Whether you work in an office or not, your agent will be able to help you in the same way that personal assistants support executives today. If your friend just had surgery, your agent will offer to send flowers and be able to order them for you.” - Bill Gates.

AI agents have come a long way. They're no longer just rule-followers but smart decision-makers. To understand this better, let's take a look at various types of agents in AI with examples:

1. Simple reflex agents

A simple reflex agent follows if-else rules to perform specific tasks. In customer experience (CX), the agent helps with:

• Answering basic inquiries

• Categorizing and labeling support tickets

• Handling FAQ responses and simple ticket routing

While efficient for repetitive tasks, the agent has no memory and cannot learn from experience, making it less adaptable to challenging situations.

2. Model-based reflex agents

Model-based AI agents go beyond simple rules. They build an internal model of the world, helping them adapt to changes and make more informed decisions. Take a robot vacuum cleaner: it detects obstacles, adjusts its path, and can remember cleaned areas to avoid repetition.

In CX, these agents can be used for:

• Automated customer service

• Personalized recommendations

While more advanced, they come with challenges such as high computational demands, difficulty handling real-world complexities, and the need for constant updates to avoid faulty memory.

3. Goal-based agents

Goal-based agents have an internal model and specific goals. They can plan and make decisions to achieve desired outcomes. Think of a self-driving car navigating traffic to reach its destination. That’s goal-based logic in action.

In customer service, goal-based agents can:

• Manage complex processes like multi-step troubleshooting and ticket escalations.

• Prioritize urgent cases that impact customer satisfaction.

• Learn from past interactions.

• Make smarter decisions, adjusting strategies as needed.

Goal-based agents are powerful but not perfect. These agents can struggle with fast-changing situations and need significant processing power to stay sharp.

4. Utility-based agents

Utility-based agents don't simply aim to get things done. They strive to do them in the best possible way. Consider them as smart decision-makers who weigh all options and choose the one that leads to the most rewarding outcome, whether that means more satisfied customers, quicker resolutions, or better use of your resources. They can serve as intelligent agents for customer service, since they:

• Prioritize inquiries, factoring in customer loyalty, issue complexity, and urgency.

• Optimize resources, ensuring the right agents handle the right queries at the right time.

• Provide personalized responses, tailoring solutions based on past interactions.

5. Learning agents

Learning agents can learn and improve from experience. They combine the features of utility-based models but adapt based on feedback. This makes them suitable for complex environments such as gaming and healthcare.

Here's how they show up in CX:

• Understanding emotions: They can sense a range of emotions in a customer's tone and send the issue to a human agent for a more nuanced resolution.

• Staying a step ahead: By recognizing patterns in past data, they can predict issues before the customers even reach out.

• Offering multilingual support: These agents improve their multilingual responses with time, learning from each interaction.

They are powerful but need significant data and computing muscle to perform well.

6. Hierarchical agents

Hierarchical agents think like accomplished managers. They break down complex goals into smaller, manageable tasks and make sure everything runs smoothly. One agent oversees the big picture, while other agents take care of the details. It's teamwork, but powered by AI.

In customer service, hierarchical agents can:

• Guide customer journey: Oversee diverse products and services across regions.

• Use a smarter route: They send issues to the appropriate levels, based on urgency, expertise, or complexity.

• Scale with ease: As your business scales, these agents manage higher volumes while maintaining quality.

Hierarchical agents are effective. But they can be tricky to design and maintain. Besides, they need clear communication across layers to avoid mix-ups.

7. Multi-agent systems (MAS)

Multi-agent systems (MAS) are like well-coordinated teams, with several AI agents working together to get things done. They can be software-based or include physical agents such as robots or sensors. They specifically excel in robotics and healthcare.

Here's how they operate:

• Support multiple departments: Route queries to the right teams across services.

• Handle high-volume interactions: Handle large interactions for enterprises with complex operations and diverse needs.

• Enhance scalability: Adjust dynamically in real-time, allocating more agents during peak hours.

However, MAS are the most complicated systems to implement, requiring advanced mechanisms to ensure all agents work seamlessly.

Selecting the right AI agent for your business

Now that you are familiar with different AI agents and their types, let’s dive into how you can choose the right one. Not all AI agents are created equal. Some are ideal for simple, repetitive tasks, while others are designed to handle complex decision-making. You should consider the factors given below while selecting the AI agent for your business:

• Business size and complexity: Larger organizations with diverse offerings may need more sophisticated agents, while smaller businesses benefit from simpler solutions.

• Support volume and types of queries: High-volume support teams may rely on reflex agents, while businesses handling complex inquiries might need goal-based or utility-driven approaches. 

• Technical resources: Advanced AI agents require higher technical expertise and infrastructure. Consider what your team can support.

• Integration with existing systems: Your AI agent should be able to seamlessly access and integrate with existing CRM systems, knowledge bases, and communication channels while meeting security and compliance standards.

• Growth projections: Select an agent that evolves with your business, adapting to new customer needs over time.

Implementation roadmap

Once you've identified the right types of AI agent for your business, it's time to move toward implementation. 

1. Assess your current system: Understand your current support landscape. Ask yourself:

   • Where do team members face the most challenges? 

   • Which customer complaints take the longest to resolve? 

   • How much time do agents spend on repetitive tasks? 

   • What’s the current resolution time, and where are the bottlenecks in repetitive tasks? 

   • How satisfied are customers with current support? 

2. Start small, learn fast: Rather than overhauling your entire support system at once, adopt a targeted approach. Address specific challenges within a set timeframe. This allows for effective feedback collection and necessary adjustments before scaling up. 

3. Plan for scale: Map out a phased expansion for your AI agent’s responsibilities. A gradual rollout ensures continuous performance monitoring and optimization, enabling your AI agent to evolve without hassle. 

4. Measure what matters: Track key performance indicators consistently to maintain alignment with your business goals. Consider: 

   • Are customers receiving faster responses?

   • Has customer satisfaction improved? 

   • How much time do AI agents save for human agents on complex issues?

By tracking these metrics, you’ll gain a clear understanding of your AI agent’s success and identify areas for future improvement.

Freshdesk's Freddy AI Agent for faster, accurate customer support

Freshdesk's Freddy AI Agent brings together multiple types of agents in AI to deliver a comprehensive customer service solution. Its technology adapts to your business needs, whether you require simple automation for routine tasks or advanced, learning-based systems that handle multilingual conversations, skill-based ticket assignments, and time zone management. 

Freddy AI Agent is built to scale across all business sizes, from start-ups and SMBs to large enterprises with complex implementations. Freshdesk AI agents allow businesses to start with basic reflex-based agents and evolve into advanced use cases as they grow.

A few examples of how Freshdesk deploys various AI agents and its types on its platform: 

Bridgestone reduced the average ticket age by 97%, saving 35+ hours, using Freshdesk’s AI agent. By using Freshdesk AI agent, the automotive manufacturing company could easily scale its support infrastructure across new geographies. 

But note that these AI agents aren't one-size-fits-all, ranging from simple to sophisticated models. Choosing the right one depends on your growth plans, issue complexity, and tech resources.

Explore Freshdesk's AI-powered solutions to transform your customer service. Book a demo today.