Agentic AI

What is Agentic AI?

Agentic AI refers to AI systems that independently pursue a goal, determine the path to a solution at runtime, and plan and execute the necessary steps within connected systems. Such a system breaks down a task into sub-steps, uses data and tools to achieve them, and evaluates each intermediate result until the goal is reached.

The term "agentic" is derived from "agent," referring to an entity capable of acting. It describes a system's ability to take independent action toward a goal.

How does Agentic AI work?

In customer service, Agentic AI is a subfield of AI automation, which automates customer interactions and service tasks using artificial intelligence. Technically, Agentic AI operates in a recurring cycle. The system receives a goal—for example, to fully resolve a customer inquiry—and breaks it down into individual steps. After each step, it evaluates the result and decides on the next one until the goal is achieved or a predefined limit is reached.

At the core of Agentic AI is usually a Large Language Model (LLM). It interprets the task, plans the approach, and selects appropriate tools. In this context, tools are interfaces to other systems, such as a legacy system, a customer database, or a knowledge base. A memory component retains the context of previous steps so that intermediate results can build upon one another.

The company defines the extent to which Agentic AI acts independently. Permissions determine which data the AI can read and which it can modify. At defined points, an employee approves individual steps or takes over the request. This principle is known as "human-in-the-loop": the AI provides support, while the human makes the decisions.

AI agents and Agentic AI: how the terms are related

An AI agent is an individual software system that independently completes a specific task using artificial intelligence. Examples include an agent that schedules appointments or one that checks incoming damage reports for completeness.

Agentic AI is the overarching term. It describes the approach and the ability to independently pursue goals over multiple steps. An AI agent is therefore a concrete application of Agentic AI. For complex tasks, multiple specialized AI agents often work together, with an agentic system coordinating their steps.

In common usage, both terms are often used interchangeably. However, in a stricter sense, an AI agent is only "agentic" if it decides on the solution path itself at runtime rather than following a pre-programmed dialogue.

Agentic AI, Generative AI, and chatbots: the differences

Agentic AI is often equated with related technologies. The core difference lies in whether a system merely provides answers or also takes action itself.

  • ‍Generative AI: Generative AI creates new content such as text, images, or code in response to an input. Agentic AI uses this capability as a building block and additionally performs actions in other systems to achieve a goal over multiple steps.
  • ‍Chatbot: A classic chatbot answers questions based on predefined dialogues or a knowledge base. Even systems based on Conversational AI primarily conduct dialogues. In contrast, an AI agent in customer service processes a request through to completion, for example by checking contract data and triggering a change in the core system.
  • ‍Rule-based automation: Rule-based automation such as Robotic Process Automation (RPA) executes a pre-programmed sequence. A simple address change can be reliably automated this way without the need for agentic AI. Agentic AI determines the path to a solution at runtime, making it suitable for requests where the process cannot be fully defined in advance.

In practice, these approaches complement each other. Fixed processes and standard questions remain with rule-based automation and chatbots, while agentic AI handles requests for which there is no predetermined path.

Agentic AI in customer service: Where AI agents are used

In customer service, agentic AI proves its value primarily for requests that involve multiple steps and multiple systems. For example, an AI agent in customer service can take a request via chat, ask follow-up questions, retrieve contract data, and record it in the core system. If it cannot complete the request, it hands it over to an employee along with the history of the interaction.

A second area of application is in the background. There, agentic AI evaluates incoming emails, letters, and documents, identifies missing information, and requests it from the customer. This ensures that employees receive requests that are already fully prepared.

Agentic AI is particularly relevant for the many rare and diverse requests that do not fit into a standard process. It is usually not worth programming a separate workflow for each of these variations. An AI agent can process such requests without every step being defined in advance.

What are the benefits of agentic AI?

Agentic AI completes requests that previously hit the limits of automation. This can increase the proportion of requests resolved without human intervention. Your employees gain time for cases that require experience and judgment.

For your customers, agentic AI means that a request can be processed to completion even outside of business hours. Follow-up questions and transfers between departments can be reduced when an AI agent gathers the necessary information from multiple systems itself.

During peak loads, agentic AI can scale with the volume without the need to train additional staff. You remain in control. Through permissions and approval points, you define what the AI is allowed to decide on its own, and a log makes every step traceable.

What to consider when introducing agentic AI

Whether agentic AI proves successful in customer service depends on a few clearly verifiable conditions.

  • ‍Suitability of requests: For requests with a fixed, easily describable process, rule-based automation is sufficient and easier to operate and audit. Agentic AI is worthwhile for requests with many variations that involve multiple systems.
  • ‍Scope of action: Before starting, it must be determined which data an AI agent is allowed to read and change, and which steps it should only perform after approval. The more consequential a step is, the more it should remain in the hands of an employee.
  • ‍Handoff to employees: If an AI agent cannot complete a request, a clear handover point is required. Ideally, the employee sees the complete history of the interaction so that the customer does not have to repeat anything.
  • ‍Traceability: Every step taken by the AI should be logged. This is the only way to review decisions later, for example in the event of complaints or during an audit.
  • ‍Data storage and operation: When dealing with personal customer data, it is necessary to clarify where language models and data are operated and who has access to them. In regulated industries such as the insurance sector, the location of data centers also plays a role.

Agentic AI in practice

In high-volume service organizations, the value of Agentic AI also depends on the handoff between the AI and employees. Anything an AI agent cannot complete must be passed on to the appropriate person along with the history of the interaction so that the resolution process does not have to start from scratch.

Dynamic Workload by IP Dynamics bundles AI applications in the AI Automation module. Conversational AI conducts automated dialogues with customers and hands them over to employees with full context. Agent Assist supports employees directly in the frontend, and the AI Foundation Hub provides the common base that allows additional AI functions to be added without switching systems.

In the Workload Management, all requests from various channels are consolidated into a central work queue and automatically assigned to the appropriate person. Dynamic Workload is operated in the IPD Sovereign Cloud, geo-redundantly in data centers in Germany.

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FAQ about Agentic AI

Häufig gestellte Fragen

In its basic form, ChatGPT is a dialogue system based on generative AI. It responds to inputs with generated text. Such a system only becomes "agentic" when it uses tools—such as researching the web or filling out forms—and plans a task itself over several steps. Some chat applications, including ChatGPT, now offer corresponding agent functions.

A multi-agent system distributes a task among several specialized AI agents that work together. In customer service, for example, one agent identifies the request, while a second checks the contract data and prepares the response. A higher-level agent coordinates the steps and consolidates the results. This division makes complex tasks more manageable, but increases the coordination effort.

Because Agentic AI acts on its own, errors have a more immediate impact than in a system that only provides answers. A language model can misjudge a situation and trigger subsequent actions in other systems. Additionally, there are manipulated inputs, known as prompt injections, and overly broad permissions. Strictly limited rights and approval points for consequential steps reduce these risks.

An AI agent in customer service handles requests that can be completed using available data and within clear authorization limits. For goodwill decisions or unclear situations, it hands over to an employee, ideally with the entire history of the interaction. This shifts the employees' work toward cases that require experience and human judgment.