Unlocking the Potential of Agentic AI: Applications Across Different Industries

You might have gotten the hang of using AI tools like ChatGPT, but have you experimented with agentic AI yet?

These tools can take more complex actions and work independently, the way a human worker might. They do have their limitations, but they go far beyond what you’re used to.

Want to know how it’s done? Here’s our guide.

What is agentic AI?

In a recent whitepaper titled Practices for Governing Agentic AI Systems, the team at OpenAI explored some of the considerations, opportunities, and ethical impacts of agentic AI. They also provided a straightforward definition for these tools:

“Agentic AI systems are characterized by the ability to take actions which consistently contribute towards achieving goals over an extended period of time, without their behavior having been specified in advance.”

One key point covered further in the paper—but not in this definition— is an agentic AI’s ability to complete complex goals even when they require taking actions on other platforms (like booking websites or work software).

This is a big part of what sets agentic AI apart from other AI tools. 

Agentic AI vs. other AI tools

In its paper, OpenAI calls this characteristic “agenticness.” It’s defined as “the degree to which a system can adaptably achieve complex goals in complex environments with limited direct supervision.” There are four primary components to agenticness, with each agentic AI tool having varying degrees of each one. These components are:

• Goal complexity: How hard would it be for a human to achieve this goal?
• Environmental complexity: Does the AI need to use multiple external tools to achieve its goal? Are there multiple stakeholders involved? What’s the time horizon for performing this task?
• Adaptability: How well does the AI tool react to change? Can it take an alternate approach to resolving an issue?
• Independent execution: How much human oversight is required to get the job done?

So while an AI tool like ChatGPT is definitely powerful, it doesn’t qualify as agentic AI because it doesn’t meet this criteria.

4 examples of agentic AI tools

Agentic AI is still a relatively young category. While popular AI models hit the mainstream at the end of 2022—leading to an explosion of AI-powered SaaS tools—agentic AI is a more recent development. Most agentic AI platforms are made for enterprise-sized organizations that can have internal developers build custom workflows with them, but some are purpose-built for smaller teams that need to deploy something that’s ready to go out of the box.

PixieBrix

PixieBrix is a low-code, AI-powered platform for automating just about anything you need in the websites and apps you use every day. Streamline workflows, increase your overall productivity, and level up your tools. The PixieBrix Marketplace is full of pre-built blueprints that’ll do anything from adding sidebars to your favorite websites to creating intelligent overlays and blocking the distractions killing your productive time.

Here are just a few examples of what you can do with PixieBrix:

• Add an AI Copilot sidebar to any page
• Bundle an AI assistant into your customer success knowledge base
• Add a sidebar that automatically translates any highlighted text
• Automatically translate text into any language

Ready to try it out? Start using PixieBrix for free here.

Microsoft’s AutoGen

This open-source solution from Microsoft is currently under active development, but developers can still jump in to build multi-agent workflows based on large-language models. The agents you build with this tool can take two forms:

• Agents that use tools by executing code developers have loaded in.
• Agents that rely on a mix of code and human involvement or feedback added in at some point in the process of completing an assigned goal.

Beam

If you’re looking for a one-stop shop for building and managing a team of AI agents, Beam might be the platform for you. This enterprise-grade solution can handle the automation of even the most complex processes. Used even in complex, high-security processes for healthcare and insurance, this platform is more than robust enough for your agentic AI needs.

Agentic

A tool specifically designed for the gaming industry, Agentic allows game developers to use AI agents to test their games without having a massive QA (quality assurance) team. All that’s needed to get an agent to thoroughly test a specific part of a game is to tell them what needs to be done in plain English. Agentic’s built-in dashboards give developers essential data for knowing which parts of their games need more testing and which ones are running properly.

9 examples of processes that can benefit from agentic AI

Now that you know what agentic AI is, how it works, and even where you can get it, let’s go over a few examples of how it can be used across your organization.

HR

HR processes are full of repetitive tasks that can easily be handled by AI agents. Here are just a few examples:

• Resume screening: AI has been used in this way for years, but AI agents take this to the next level. With the ability to work across tools and respond to feedback, they become particularly useful when more collaboration is needed for screening applicants.
• Personalized training programs: Agentic AI can combine training resources from multiple sources according to an employee’s specific needs.
• Feedback analysis: Agentic AI can centralize performance data and deliver on-demand insights to HR professionals and managers.

Customer Support

Customer support agents work with tough requirements, making AI agents a crucial ally for this essential role:

• Co-pilots and assistants: AI agents can take questions from customer support agents and pull answers from knowledge bases, past tickets, and more.
• Training: Agentic AI can be used to create training scenarios that respond dynamically to a trainee’s actions.
• Sentiment analysis: An AI agent can pick up on the essence of a message and suggest the right course of action to a customer support agent.

Sales and Marketing

Both salespeople and marketers drown in a ton of tasks essential to growing the business’s bottom line. Here are some ways agentic AI can help:

• Lead qualification: Having a ton of leads is great, but not all of them are worth your sales team’s attention. AI agents can cross-reference your leads with high-value characteristics so your salespeople only deal with the right leads.
• Campaign planning: Imagine having a copilot for planning every aspect of your marketing campaigns. An AI agent can crawl through your organization’s historical successes (and failures) to suggest the best course of action.
• Virtual sales assistant: Sales calls are high-pressure and can go South in an instant. With a virtual assistant powered by agentic AI, your salespeople can close more deals in less time.

Ready to get more done?

Agentic AI is the next best thing in AI, being able to handle more complex workflows across multiple platforms, just like a person might. Whether you want a tool that does it all for you or one that allows you to build your own agents, you’ll be getting in on a powerful trend early—reaping the rewards.

As the field of artificial intelligence continues to evolve, the concept of agentic AI is becoming increasingly significant. By definition, agentic AI refers to systems that operate autonomously, making decisions and taking actions on behalf of users. The agentic meaning in this context is deeply rooted in the ability of these AI systems to act independently, embodying an agentic state where they can navigate complex environments and solve problems without constant human intervention. This capability is transforming industries, from customer service to healthcare, where agentic workflows are streamlining operations and enhancing efficiency.

One of the most exciting developments in this space is the integration of agentic AI with Retrieval-Augmented Generation (RAG) models, resulting in what's known as agentic RAG. These systems not only retrieve relevant information but also generate contextually appropriate responses, making them powerful tools for decision-making and problem-solving. As organizations continue to explore the potential of agentic AI, understanding its agentic definition and applications will be crucial in harnessing its full potential. The future of AI lies in its ability to act as an independent agent, driving innovation and improving outcomes across various domains.