Agentic AI
Definition, how it works and use cases in customer service
Definition
Agentic AI refers to AI systems that plan a goal on their own, derive the necessary intermediate steps and execute external tools (APIs, databases, forms) – without a human having to approve every step. Unlike classic chatbots, agentic AI systems don't just respond to individual inputs but act proactively across several steps until the task is complete.
Agentic AI systems plan and execute goals on their own – without human approval per step. Unlike classic chatbots, they don't just respond, they act: they call external systems, keep context and fully resolve multi-step tasks. In customer service, agentic AI systems fully automate standard processes such as order status, returns and password resets – and hand complex cases over to human agents with full context.
How does agentic AI work?
Agentic AI systems consist of four core components. Only the interplay of all four makes a system a true agent – an LLM alone isn't enough.
1. Large language model (LLM)
A language model such as GPT-4o, Mistral or Llama understands the context of a request, interprets the goal and formulates a solution plan in natural language. On its own, the LLM is passive – it answers but doesn't act.
2. Tool use
Tool use gives the model the ability to address external systems: query databases, retrieve order status from Xentral or JTL, create tickets in Zendesk, process returns via returnless, fill in forms, send emails. Only tool use turns a language model into an acting agent.
3. Memory (RAG)
Memory enables context across several steps – as short-term memory within a conversation or as long-term memory via a vector database. Retrieval-augmented generation (RAG) also prevents hallucinations because the model only accesses verified knowledge data.
4. Planning
Planning is the real core: the system breaks a higher-level goal into sub-steps, decides on its own which tool to use in which order and corrects the plan when errors occur – without manual intervention.
A concrete example from e-commerce customer service: a customer writes "I'd like to return my parcel." An agentic AI system independently checks the order status, creates the return label, sends it by email and closes the ticket in Zendesk – in one go, without human approval per step.
Agentic AI vs. classic chatbot vs. RPA vs. conversational AI
The four terms are often used interchangeably in the market. That's misleading – the differences are technical and decisive for buying.
A rule-based chatbot follows fixed decision trees and has only limited language understanding. It can't call external systems, build contextual memory or solve multi-step tasks. Its use cases are simple FAQ answers and opening hours – anything that doesn't require action.
Conversational AI goes a step further: it understands natural language and holds real dialogs. It can keep context within a session and address external systems to a limited extent. The key difference from agentic AI, however, is that conversational AI doesn't do independent planning – it responds but doesn't plan. What conversational AI means in detail is explained in the article Conversational AI – how human can machines be?
RPA (robotic process automation) automates rigid, rule-based processes – such as filling in forms according to a fixed scheme. RPA can call external systems but works without language understanding and without contextual memory. As soon as an input field changes or an exception occurs, RPA breaks down. Agentic AI, on the other hand, interprets variable context, makes decisions and adapts the plan. In practice the two technologies complement each other – but they are not substitutes.
Agentic AI combines all capabilities: it understands language, calls external systems, keeps context across several steps and completes entire tasks on its own – even when exceptions occur. The EU AI Act (Regulation 2024/1689) is relevant for agentic AI systems in customer service in every case: users must be able to recognize that they are interacting with an AI system (Art. 52).
Use cases in customer service
Agentic AI is suited to all requests that consist of several defined steps and follow clear rules. In customer service, these are mainly recurring standard processes with a backend connection.
Order status and shipping informationThe agent retrieves real-time data from the ERP system – for example via Xentral or JTL – and answers directly, without an employee having to look up the order.
Returns processingFrom checking the return deadline and creating the label to booking it in the system, the agent handles the entire process chain – directly via native integrations such as returnless or Loop Returns.
Password reset and account accessIdentity verification, sending the reset link and the confirmation email run fully automatically without manual intervention.
First-level support with helpdesk handoverStandard requests are handled completely; complex cases are handed over to a human agent with the full conversation context. Providers like melibo that offer native connectors to Zendesk, GREYHOUND and Zammad enable this handover without manual data entry or middleware.
Order status inquiries in B2BIn B2B customer service, buyers often ask about delivery dates, partial deliveries or invoice status. An agentic AI system with an ERP connection answers these requests directly from the system – without waiting for a clerk.
According to Gartner's "Conversational AI in Customer Service" 2025, around 40% of all incoming customer service tickets in suitable industries will be fully handled by agentic AI systems by 2026. Another 20–25% can be accelerated with agent assist – where the human makes the final decision.
Limits and risks of agentic AI
Agentic AI isn't a cure-all. There are three limitations you should know before investing in a system.
Limited scopeAgentic AI systems are only as good as the tools and data they can access. Without native integrations to store or ERP systems, manual gaps appear in the process – the agent can't act on what it can't see. The melibo integrations page gives an overview of all available connections.
Risk of hallucinationWithout a RAG architecture, the underlying LLM can make up information. Only systems with secured knowledge access are suitable for production-grade customer service deployments.
Regulatory requirementsSince 2025, the EU AI Act (Regulation 2024/1689) has required that users must be able to recognize when they are interacting with an AI system (Art. 52). For DACH companies, the GDPR also applies: personal data may only be processed on the basis of a data processing agreement (DPA). What to consider for GDPR-compliant use is explained in the article How to use chatbots in your company in a GDPR-compliant way.
Conclusion
Agentic AI clearly differs from chatbots, conversational AI and RPA – the difference lies not in language but in the ability to act. The technology is mature, the integrations are available, and the regulatory requirements of the GDPR and EU AI Act can be met. If you want to get started, you'll find a structured entry point in the article AI agents in service: how they change processes – and how to get started.
