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Organizations around the world have incorporated AI technology into their business models to improve productivity. Agentic AI is an exciting innovation that helps businesses solve problems, complete routine tasks, and provide high-quality customer service.
Read on for agentic AI, explained: how it works, how it differs from other types of AI technology, and why it’s such a powerful tool for growing businesses.
Agentic AI is a system that runs as an autonomous agent, recognizing patterns and making decisions on its own.
Agentic AI models are programmed to complete specific tasks, such as providing customer service or selling a specific product. These models are much more advanced than standard chatbots, as they can handle complex multi-step workflows that other AI tools cannot.
These AI agents learn from each interaction and can even search the internet for information related to their goals. They adapt and improve over time on their own, without requiring manual training. Agentic AI tools are also built on Large Language Models (LLMs), so they can mimic natural human interactions.
Agentic AI systems start with an underlying LLM as a foundation for conversational language. These systems are usually deployed through a pre-built platform, built internally or through a partner, allowing organizations to define agentic AI tasks, workflows, and objects for the agent to complete.
From there, the AI agent uses pre-established planning and reasoning skills to decide how to proceed. It will break down complex tasks into multiple steps to work more efficiently.
Then, the AI works proactively to achieve its goals, rather than waiting for a programmer to provide further instructions. AI agents have memory, so they learn from every interaction and use new information to improve. When completing tasks, they will often search for patterns or valuable information from previous interactions to help them.
AI agents will also interact with other digital tools as they complete tasks. Depending on how they’re programmed, they can search through databases or even search the web for the information they need. Some AI agents also use APIs to connect with SaaS platforms to support specific workflows.
Agentic AI models can handle several tasks at the same time, taking feedback from users in real time to provide better output.
If you’ve used tools like ChatGPT, Claude, or Gemini in the past, you might be wondering: compared to the agentic AI definition, what is generative AI?
While these tools may seem similar at first glance, they have very different purposes. Generative AI tools focus on creating content like text, images, or code based on user prompts. Agentic AI, on the other hand, focuses on completing complex tasks and solving problems.
Both generative and agentic AI models use LLMs as a foundation and improve over time based on feedback provided. However, generative AI tools require manual prompting to get the result you want. They also have limited reasoning skills, so they can’t always solve complex problems or complete multi-step tasks.
Agentic AI works autonomously and doesn’t require ongoing prompting. This means it can complete requests the way a human customer service agent would, or even automate entire workflows for busy operations teams.
Say you’re preparing to order food at a fast-casual restaurant. A generative AI tool could answer questions about specific dishes when prompted, but couldn’t complete your order on its own. However, an AI agent could answer questions about the menu, proactively provide recommendations, send your order to the kitchen, and guide you through the payment process.
Agentic AI systems are complex and rely on several core capabilities to operate autonomously. Here are some of the key characteristics of agentic AI models:
Agentic AI tools have advanced reasoning and planning capabilities that help them handle complex workflows. They start by breaking down the workflow into smaller, more manageable tasks and putting those tasks in order.
Then, the AI agent uses reasoning to execute those tasks. Agentic AI models are programmed with sophisticated logic patterns, helping them solve problems efficiently and consistently. Over time, the AI learns what works and what doesn’t, using that information to complete tasks faster and produce higher-quality output.
Another key characteristic of agentic AI tools is interaction with other digital resources to solve problems. For example, many programmers use APIs to connect agentic AI models with other tools in their tech stack. An agentic AI tool in a retail setting might manage customer orders, answer phone calls, or assist customers in navigating a store.
Agentic AI tools can also integrate with internal databases or even external web search platforms to find information. When given a task, the agent automatically searches through this available data for helpful context to support the workflow or execute a task.
AI agents can run autonomously once they’re programmed, but they also collaborate very effectively with humans to get the job done faster. This is because agentic AI is built on LLMs for natural, conversational interactions. AI agents are also designed to take feedback, so they’re always improving to better meet human preferences.
For example, a sales team might implement an AI agent to help them find and qualify leads, then reach out to them with an initial pitch. If the prospect is interested, the human sales pros step in to close the deal. The sales team also provides ongoing input to make sure the AI agent stays aligned with the company’s current sales goals and product line.
One of the biggest benefits of agentic AI is its versatility across a variety of industries and use cases. Here are just a few of the many ways organizations are implementing agentic AI.
A conversational AI platform can handle many of the same tasks as human customer support teams. This makes it an ideal solution for handling queries from both customers and employees. Conversational AI also frees up your customer service team to focus on complex, time-consuming tasks, rather than routine requests.
Conversational AI agents can adapt to specific industry needs. For example, banks, healthcare providers, insurance companies, and utility providers all use agentic AI to help customers solve problems and find appropriate products and services.
Many organizations also use conversational AI to support internal operations. AI agents can help with things like IT support or HR requests, so they don’t disrupt day-to-day workflows.
At busy fast-casual restaurants, customers want to get their food quickly and with minimal hassle. That’s why many restaurant brands are implementing agentic AI ordering systems. These AI agents take orders at the drive-thru or from a smart kiosk inside your restaurant.
With AI agents processing orders, your employees have more time to make high-quality food and drinks. Customers get their orders faster, and your restaurant can handle more customers per day.
Vehicle brands are implementing agentic AI for automotive use. These smart voice assistants can answer questions and provide support while the user is driving and focusing on the road.
If a driver’s concerned about a “check engine” warning, the AI can explain why that warning has popped up and what to do next. When drivers are traveling to a new area, they can ask the AI for directions and local recommendations. Agentic AI can even help drivers make purchases or order takeout while they’re on the go.
With agentic AI, customers can make purchases just by talking to a voice assistant. The AI agent helps customers find appropriate products and finalize the payment with just a few voice commands. In some settings, agentic AI can also set up shipments so customers get the products they need delivered to their door.
Some tech-savvy consumers are also taking things a step further by setting up AI shopping agents. These agents review products and make purchases automatically based on a customer’s pre-set preferences. With this technology, busy consumers don’t have to handle their own grocery shopping.
Agentic AI supports IT service management by automatically processing support tickets and fixing issues before they become user problems. With agentic AI, the need for human involvement is largely removed from basic tasks like resetting passwords, fixing devices, and closing out support tickets.
Since agentic AI works seamlessly with IT infrastructure at any company, it can monitor the health of those IT systems, diagnose any problems that might occur, and even take corrective action to solve the problem before the user is impacted. Agentic AI also makes the IT team more efficient, letting them focus on other projects instead of supporting employees with repetitive tasks.
Agentic AI is incredibly beneficial for businesses, as it improves productivity and enhances customer experiences. Here are some of the benefits:
Busy teams can implement agentic AI to automatically complete routine tasks. This keeps day-to-day operations running quickly and smoothly. Instead of spending their days switching back and forth between manual tasks, employees can focus on more valuable strategic work.
Agentic AI is particularly valuable for customer service. Instead of waiting on the phone or in line to speak to a human agent, customers can get support from an AI agent immediately.
AI agents can handle most standard customer service requests, and they’ll route customers to a human agent when necessary. With this approach, companies can process requests faster to keep customers happy.
Agentic AI models can help you do more with the data you’re already collecting. You can connect AI agents to your data warehouses using an API. Once this integration is set up, you can program the AI agent to proactively review the data, identify any patterns or changes, and share helpful insights. Since these AI agents run 24/7, they help you spot consumer trends or financial patterns early to stay ahead of the competition.
Organizations can implement agentic AI into their websites and digital tools to engage with customers in new and innovative ways. For example, AI agents on a travel website might provide recommendations for hotels, restaurants, and activities based on the visitor’s preferences. This personalized experience keeps customers engaged and encourages them to spend more than they otherwise would have.
Although agentic AI is a relatively new technology, companies around the world have already incorporated it into their operations. It’s particularly popular for customer service; for example, fast food chain White Castle implemented a custom voice AI agent to take orders at the drive-thru.
Many brands are also developing smart products featuring agentic AI. Global car brand Stellantis has added voice AI to cars in several European countries, giving drivers access to a smart assistant while they’re on the go. The Medical University of South Carolina (MUSC) Health addressed a shortage of patient engagement with AI agents, and Qualitas leveraged AI to route claims and give customer service representatives more time to focus on cases.
Agentic AI also has many applications in the corporate world. Many organizations have built custom AI agents to handle sales outreach, hiring and onboarding, tech support, and much more.
The possibilities for growth and productivity are endless with agentic AI, meaning now is the perfect time to implement it at your organization. With SoundHound’s voice AI solutions, you can create custom agentic AI experiences that your customers will love.
SoundHound’s advanced voice AI technology helps you build intelligent agents that can solve problems with sophisticated reasoning and natural language capabilities. These agents integrate with your existing systems to handle complex tasks without manual input.
Talk to an expert today to see how SoundHound’s agentic AI can transform your operations.
What is an agentic artificial intelligence example?
An example of agentic AI is a financial institution that uses AI customer service agents to handle issues like card replacements and even fraud. The AI agent interacts with customers over the phone or through a web chat, similar to how a human agent would.
How does agentic AI differ from generative AI?
The main difference between agentic AI and generative AI is that agentic AI works autonomously once it’s been programmed, while generative AI requires ongoing prompting to generate results. Agentic AI proactively seeks out new information and completes tasks on its own.
What are the limitations of agentic AI?
The biggest limitation of agentic AI is that it can make mistakes that affect your customers or your operations. Even the most sophisticated AI agents can hallucinate false information. Because these models are so complex, it’s also difficult to find and address the root causes of these problems when they occur.
What are common features of agentic AI platforms?
The most common features of agentic AI platforms are proactive reasoning, long-term memory, API connectivity, and collaboration with both humans and other agents.
