What Is Agentic AI? Understanding the Next Leap Forward

Vonage shares an agentic AI definition, explain what the technology can do, and explore some challenges and use cases.

Many businesses use AI to automate basic tasks or handle customer FAQs. But what if AI could learn to operate alone, making decisions and adapting to changing situations?

That’s exactly what agentic AI does.

What is Agentic AI?

We can define agentic AI as a type of artificial intelligence capable of making its own decisions without constant input from humans.

This technology can gather and analyse vast amounts of data from multiple sources and autonomously perform complex, multi-step tasks.

Agentic AI systems are made up of AI agents that can interact with a wide variety of other tools and software. These AI agents can use problem-solving, reasoning, and ongoing learning to develop strategies and adapt to different situations.

In contrast to rules-based AI assistants, agentic AI acts in alignment with predefined objectives rather than responding to a set of instructions. It can interpret a user’s goal and make decisions based on context.

Agentic AI vs. generative AI: What’s the difference?

The two types of AI have some similarities. For instance, they can both analyze huge datasets to extract relevant information and perform tasks on behalf of humans.

But their outputs are different: generative AI creates new content, while agentic AI tools generate actions and decisions.

Generative AI can identify and encode patterns in its training data and replicate those patterns in its output. When a user makes a specific request or query, the AI will come up with original content such as text, images, video, or audio in response.

GenAI usually works on narrow, well-defined tasks, while agentic AI is capable of performing complex workflows without fixed instructions or human prompts.

It doesn’t need to be told how to complete a task, but considers its preset objectives and makes informed choices.

In essence, generative AI is a deterministic and reactive technology where agentic AI is probabilistic and proactive.

How Does Agentic AI Work?

Agentic AI uses a combination of machine learning, natural language processing (NLP), large language models (LLMs), and automation technologies.

Perceive

AI agents can perceive their environment and gather and process data from a wide range of sources, which might include databases, user interactions, or IoT sensors. They can recognize objects and extract meaningful features, identifying what’s relevant to the task or query.

Reason

Agentic AI then processes the data and uses its reasoning abilities to make appropriate decisions based on the information.

LLM and NLP technology enable the AI agents to understand requests and their context, and generate strategies for meeting the predefined objectives.

Act

The agentic AI now acts with autonomy, carrying out the required tasks based on the decisions and plans it has made, and on its learned experience.

This includes automating complex workflows connecting different systems and integration with external tools and systems via APIs.

Learn

Through machine learning, agentic AI learns from all the data it has received and becomes smarter over time. It can refine its abilities and adapt to new situations. It also uses a feedback loop where its own outputs are fed back into the system to improve models.

Benefits of Agentic AI

Here’s what agentic AI software does for businesses:

Increases Efficiency

As agentic AI can act on its own, it saves time and reduces operational costs. For example, it can read and analyse huge amounts of data much faster than humans, and automate tasks so that humans are free to focus on creative or strategic activities. This boosts productivity and improves overall performance.

Enables Smarter Decisions

Agentic AI can analyse data in real-time, so you always have the most updated insight for data-driven decisions. It identifies patterns and trends in the data and makes accurate forecasts.

Handles Complex Tasks

This type of AI doesn’t just take care of routine tasks, but executes multi-step workflows autonomously. For example, it can manage complex supply chain logistics and make adjustments as global conditions evolve. Agentic AI can also perform granular tasks, leading to greater workforce specialization.

Improves Customer Experience

Agentic AI can deliver a better customer experience by predicting their needs and personalizing interactions.

With LLMs, it can understand context and sentiment, and generate human-like text that sounds natural. In contact centres, AI agents can respond quickly and at scale, operating around the clock for consistent support.

Works in a Team

In multi-agent systems, various AI agents are trained to take on different tasks or subtasks, and they work together as a team through agentic orchestration.

You can add extra agents to the system as needed. Agentic AI can also integrate with other business systems and collaborate with a human workforce.

Learns Continuously

Agentic AI keeps learning and adapting over time, meaning your business will always have the insights needed to respond quickly to market changes or different customer behaviours and preferences.

Real-World Agentic AI Examples

Agentic AI isn’t just for smart assistants and autonomous vehicles. There are many agentic AI use cases, from manufacturing to software development to financial services to content creation. Here are a few examples:

Customer Service

The first AI virtual assistants were pre-programmed with a limited range of responses, but agentic AI means that AI agents can communicate with customers in a more agile way.

They can detect sentiment and intent, and resolve issues and queries autonomously to improve response times. They’re also proactive in making recommendations based on customer preferences.

Sales

Agentic AI can work alongside human sales reps for increased efficiency and engagement.

For example, Salesforce’s Agentforce Service Development Rep can interpret customer messages and generate responses in the company’s brand voice, as well as help reps by recommending follow-up actions and booking meetings.

Healthcare

Agentic AI can extract critical information from big data and automate admin tasks so medics can focus on patient connections. It can suggest treatment plans as well play a role in the discovery and development of new drugs.

Challenges to Implementing Agentic AI Frameworks

While agentic AI has huge potential benefits for businesses, there are various challenges to overcome as the tech continues to evolve.

Agentic AI systems are still in their infancy and don’t yet have emotional intelligence or moral reasoning capabilities. They also have the potential for errors and bias, especially if the data they’re trained on is outdated, biased, or incomplete.

AI agents need high-quality and diverse training datasets, which is more difficult for small or new businesses that haven’t yet generated a lot of data.

There are also questions about the ethics of using agentic AI. For instance, who would be accountable if an autonomous system makes a serious mistake?

Again, humans are needed to ensure that agentic AI operates ethically and that the business takes appropriate steps to protect sensitive data.

Agentic AI can easily go off track if it doesn’t have clear goals. As it can learn and evolve by itself, you must govern it carefully to ensure it stays in its lane and doesn’t stray from the intended purpose.

As this tech is still evolving, it’s hard for governments and regulatory bodies to put the right regulations and laws in place to govern it. This means uncertainty for organizations about what they can and can’t do with AI.

It’s important to assess the likely impact of agentic AI before you unleash it on your business. Otherwise, you’ll fail to spot potential flaws, and you may end up with consequences you didn’t plan for.

Consider which departments, processes, and data will be affected, and ensure that AI fits into your overall business strategy.

You’ll need to be aware of the potential disruption to business as you integrate agentic AI architecture with existing systems. If you have legacy systems in place, they might need to be updated or replaced to work with new technology.

Agentic AI is a complex technology, and it can be difficult for humans to understand how it makes decisions. It’s important to be transparent about your use of AI and its benefits, so that employees and stakeholders recognize its purpose and know how to use it safely.

What Does the Future of Agentic AI Look Like?

In a recent Deloitte survey, 26% of respondents said their organizations were already exploring autonomous agent development to a large or very large extent.

According to Gartner, 33% of enterprise software applications will include agentic AI by 2028, enabling 15% of day-to-day work decisions to be made autonomously.

Agentic AI will likely become even more sophisticated in terms of its ability to make decisions and have natural-sounding interactions with users. The technology is becoming more adaptable and better at contextual understanding.

Collaboration between multiple AI systems will allow them to solve complex problems and manage interconnected workflows. As generative AI also continues to evolve, companies will be able to use it in combination with agentic AI.

For example, if you’re limited by data scarcity or privacy concerns, you can use generative AI to create synthetic datasets (realistic but free of personal information) and use them to train agentic AI systems.

Agentic AI: Final Thoughts

The future of agentic AI is brimming with potential. Working autonomously, it boosts productivity by handling a range of tasks and increases customer engagement by delivering valuable insights and enabling fast, personalized responses.

When you integrate agentic AI, make sure you do it in an ethical, responsible, and transparent way that minimizes disruption to your operations.

If you get it right, AI agents can fit seamlessly into your processes and work alongside human employees for improved overall performance.

Reviewed by: Rachael Trickey