Agentic AI has become one of the hottest buzzwords in technology today. As is typical with new capabilities, agentic AI has no clear, agreed upon definition. To add to the confusion, some of the big players in the market have each created a different definition of agentic AI.

This blog was written by Melissa Swartz, an independent consultant and expert on communication technology at Swartz Consulting, LLC and Global Tech.

Agentic AI has become one of the hottest buzzwords in technology today. As is typical with new capabilities, agentic AI has no clear, agreed upon definition. To add to the confusion, some of the big players in the market have each created a different definition of agentic AI.

Cynically, this confusion could be seen as a mechanism used by vendors to set the stage to highlight capabilities that they provide, while downplaying those that are not offered.

Alternatively, confusion might be reduced as multiple “flavors” of artificial intelligence (AI) agents emerge as technology matures, allowing for flexibly segmented development.

This leaves many asking: Is agentic AI a breakthrough technology or just the latest hype?

Let’s dig a little deeper and see if we can find some answers.

Defining Agentic AI

According to this video from Google, “No one seems to agree on exactly what an agent is.” It makes sense to think of agentic capabilities as a continuum of capability and complexity.

Not all agents will be equally complex; some will perform very simple tasks. Others may have greater capabilities and the ability to handle more complexity (See figure below).

However, there are some characteristics that are generally considered to be capabilities of agentic AI:

  • Agents exist within a specific environment.
  • They have one or more goals to accomplish.
  • Agents are able to sense inputs or stimulus in their environment.
  • They are able to reason about the things that they sense and decide on a course of action based on the stimulus.
  • Agents can act autonomously to accomplish a specific goal. Actions can include:
    • Providing notifications if specific conditions are met.
    • Reviewing documents and providing an answer to a question.
    • Handing off the interaction to another agent or a human.
    • Updating records, canceling orders or kicking off other processes.

In short: Agents can sense, decide and act within their environment to fulfill a given agenda.

Additionally, they might be able to interact with other AI agents, analyze and improve their performance over time; have dialogue capabilities; act proactively; and/or affect the real world by taking actions, such as preventing a financial transaction when fraud is detected or placing orders to restock for low inventory. 

According to Google Gemini, agentic AI refers to a class of artificial intelligence systems designed to operate autonomously, make decisions and perform tasks without constant human intervention. The term “agentic” highlights their “agency” — their capacity to act independently and with purpose towards a goal.

How Is Agentic AI Different From Traditional AI?

Google Gemini notes that “agentic AI represents a significant step forward from traditional AI, moving towards systems that are not just intelligent but also capable of independent action, planning, and continuous self-improvement in dynamic environments.”

Traditional applications rely on a set of predefined rules to make decisions or perform simple tasks and are typically scripted. The rules and scripts control the actions.

Researchers in the multi-agent systems field contend that legacy “agents” that follow pre-defined rules are just programs — and are not truly agentic.

Agentic AI doesn’t follow a script. Instead, it’s given a goal. It then determines the best route to reach that goal. Typically, the agentic AI is given parameters, such as a set of tools, instructions on how to use them and a description of how to execute the action.

Agentic capabilities include the ability to evaluate different scenarios and predict the outcomes of various actions. They can even assign a value to each action based on how well it aligns with the agent’s goals. By doing so, the agent acts autonomously to choose the action that’s most likely to achieve its goals.

Agentic AI uses large language models (LLMs) to understand inputs it receives and to communicate via dialogue. These AI agents can provide responses based on the context of a conversation, including information previously provided. This greatly improves the perceived quality of the interaction. 

For example, if a car insurance policyholder asks, “Can I add my new Chevy to the policy?” the AI can respond with “Sure. To add your Chevy, I’ll need to know…” This confirms, in a fluid way, that the transaction is progressing properly.

Users can add follow-up questions, and the agent will “remember” and incorporate previous inputs. For example, they can ask: “It’s a 1968 model. Is special insurance required?” Here, the agent would “remember” that the vehicle is a Chevy and investigate to provide an answer.

“Can you also add my Nissan?” The agent recognizes that this is a new request to add an additional vehicle. Agentic AI can handle this smoothly, whereas a scripted interaction would require the user to return to an earlier part of the transaction to start down this new path.

“For the Nissan, I only need Collision and Liability, not Comprehensive. How much will my rate increase?”

The agent can respond to this request by looking up the information, obtaining additional information if needed and calculating the amount to provide an answer.

How Organizations Can Use Agentic AI  

Agentic AI can be used in many ways, across functions and industries. From a customer experience perspective, agentic AI can be used:

To extend the capabilities of traditional chatbots to provide true problem resolution:

  • Retrieve customer data, history, and knowledge base information
  • Deliver context-specific responses or pass customers to the appropriate resources for further assistance
  • Take action to resolve a problem, update records, provide status information, etc.

For determining customer intent, even in situations where the information provided is complex or vague:

  • Ask follow-up questions to gain more context, based on each individual situation
  • Determine if there is a question to be answered or if action is required
  • Retrieve information to answer the question or route to another agent/program/person to fulfill the action
  • Communicate with requestor regarding status or to obtain additional info needed to progress through a workflow
  • Select the right tool to perform the required action

To operate across channels (voice, email, chat, SMS, etc.):

  • Access full customer history and context from multiple sources (order history, chat history, previous calls, emails)
  • Provide follow-up information via customer’s preferred channel (SMS, chat, email)

To automate documentation of interactions, including:

  • Interaction summarization
  • Trend analysis

Proactive outbound contacts:

  • Follow-up information
  • Status updates
  • Reminders

Understanding the Benefits of Agentic AI 

At a high level, some of the benefits that agentic AI can offer for customer experience include:

  • Automation of more complex processes than simple scripting programs can handle
  • Improved, nuanced dialogue that appears natural to customers/users
  • Offloading of mundane tasks from humans, allowing them to handle more complex tasks or those requiring empathy or creative thinking
  • Faster results and outcomes, as automated processes are no longer delayed by waiting on humans to move to the next step
  • Consistent quality
  • Ongoing improvement
  • 24x7x365 operation
  • Handling spikes in volume that would overload traditional operations

Drawbacks of Agentic AI in CX

As with everything, there are drawbacks with the use of agentic AI in customer experience. It’s much like using a blowtorch. In the right situation, a blowtorch is a fantastic tool. But if used incorrectly, it can burn down a building.

Here are some potential risks that must be mitigated when using agentic AI.

Garbage In, Garbage Out

Before you automate, it’s imperative that your existing processes are optimized and well documented. Underlying data should be clean, classified and accessible. Optimizing your processes and data can be challenging. Don’t underestimate the level of effort necessary. 

Complexity

Supporting agentic AI requires staff with the skills necessary for managing and governing AI agents.  There can be many ongoing support challenges due to their technical complexity.

They must be managed and optimized on an ongoing basis to ensure that they are operating as expected and don’t drift or experience downgraded quality. Testing and monitoring can be challenging as agentic AI applications and LLMs don’t provide the exact same output in every iteration.

Sabotage

Employees who are fearful of future job loss may sabotage AI rollouts.

The Usual Suspects

AI has other risks that must be mitigated, such as:

While the increased autonomy and reasoning power of agentic AI is a major advantage, it can also be a source of unpredictability. It’s important for organizations to have clear guardrails and oversight to be sure this independence remains aligned with business goals – and doesn’t drive off course.

Building Your Foundation for Agentic AI

Agentic AI is a powerful technology that promises automation of increasingly complex workflows. At the same time, poor implementations, a lack of understanding on costs and overall value, or lax security have the potential to create a fiasco. It’s important to ensure that your organization has a strong foundation from which to build AI capabilities.

Global Tech offers an online quiz that will provide a high-level score of your organization’s AI readiness. We also assist in identifying and assessing the viability of AI use cases and creating strategic plans for implanting AI.

Genesys Cloud™ AI Studio makes it easier to build and scale AI-powered experiences in the era of agentic AI. With it, anyone in your organization can drive better customer experience (CX) across every conversation. Learn how you can use Genesys Cloud AI Studio to deploy AI faster with a centralized place for creating, configuring and managing the technology.