What Is Agentic AI? Understanding the Next Leap Forward
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. In this post, we’ll share an agentic AI definition, explain what the technology can do, and explore some challenges and use cases.
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 analyze vast amounts of data from multiple sources and autonomously perform complex, multi-step tasks.Â
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. Â
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 analyze 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 analyze 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 centers, 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 behaviors and preferences.
Get the data
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.Â
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. Â
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.Â
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.
Vonage is looking to the future by expanding its collaboration with SAP, integrating agentic AI and network APIs with SAP Business AI capabilities. The partners have teamed up with Accenture to showcase a proof-of-concept (POC) demonstrating a multimodal application of agentic AI, integrated with Vonage APIs.Â
This will highlight how AI can amplify operational efficiency and customer experience through real-time network and communication-enabled insights, and show how powerful AI agents will be able to handle multi-step processes.
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.
Vonage makes it easy to add AI into your customer communications and workflows, whether deploying virtual assistants or connecting your data with analytics tools. The technology helps human agents work smarter and engages every customer in natural language on any channel.
Visit the Vonage AI Hub to learn more about building AI-driven customer experiences.
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Still have questions about agentic AI?
Whether or not you have software development skills, you can build agentic AI in the Vonage AI Studio. The low-code/no-code flow builder makes it easy to create and deploy omnichannel conversation flows that integrate with your platform.
You can consult with expert Vonage AI conversation designers for insights and recommendations, or let the team build and manage an implementation to meet your unique needs.
If you have a high volume of customer interactions but not enough resources to handle them, Agentic AI can help you by automating basic and complex tasks. This frees up human agents for other activities and serves customers outside of office hours.
Agentic AI also helps with customer engagement by responding to requests and queries promptly and in a human-like way. It also uses your data to make smart decisions, saving you hours of annual analysis.
An agentic workflow is an AI-driven process in which AI agents carry out multi-step tasks by themselves. However, this differs from basic automation, where the AI can only follow pre-programmed instructions. Agentic AI adapts to a given situation and uses its reasoning capabilities to take the most appropriate action at each step.
Although contact center workers may be concerned about being replaced, we’re nowhere near that scenario. Agentic AI is still a new technology, requiring human oversight and intervention to ensure it’s behaving as it should.
For example, humans need to set objectives for AI agents and review their output for errors and bias. And there are plenty of times when a human is best-placed to handle a complex inquiry or customer complaint. Especially when some customers have concerns about AI usage.
The idea is that AI agents work alongside their human counterparts to enhance performance and efficiency. They’re not ready to take over completely, nor would we want them to.
