What is Agentic AI?
Agentic AI refers to AI systems that not only respond, but also pursue goals independently and plan and carry out multi-step actions.
This is precisely where Agentic AI differs from traditional chatbots: it acts rather than reacts. An Agentic AI system recognises a customer’s enquiry and links information from multiple systems to make a decision and resolve the matter. This could involve, for example, amending an order, booking an appointment or concluding a contract.
"The more interfaces the AI agent has at its disposal to interact with the customer’s environment, the greater the potential for the AI agent to resolve the enquiry independently for the user making the enquiry within the customer solution in future."
An example from customer service: A customer has moved house, and her order has not yet been dispatched. She would like to change the delivery address as soon as possible.
- A traditional chatbot links to the FAQ page on changing your address.
- A generative AI with a knowledge base explains in simple terms how the address change works.
- An AI agent checks the order status in the shop system, realises that the order has not yet been dispatched, asks for the new address, updates it in the system and confirms the change to the customer – directly in the chat, without any disruption.
The issue has been resolved without the need for a member of staff to intervene. We have described exactly how this works here as an AI workflow.
Agentive or generative? An overview of the key terms
These four terms are often used interchangeably, but they describe different levels:
The chatbot isn’t going away; it’s becoming the interface through which customers interact with AI agents.
How does Agentic AI work?
Agent-based AI operates in a recurring four-step cycle. This process enables it to tackle complex tasks independently and in a goal-oriented manner:
- Capture: The agent captures the enquiry, including its context, using data from various sources (e.g. CRM, knowledge base, ticketing system, ordering system).
- Decide: The system assesses the situation based on the context and plans the next steps accordingly.
- Execute: Actions are carried out autonomously via integrations such as APIs, webhooks or the Model Context Protocol (MCP), e.g. amending an order.
- Optimise: The results form the basis for further optimisation. This is achieved, amongst other things, through structured feedback and defined approval processes.
The key point is this: the agent can only use the actions that have been made available to them and authorised for them. This ensures that their scope for action remains clearly defined at all times.
What technical components are required?
- Large Language Models (LLMs) provide language comprehension and decision-making logic.
- Retrieval Augmented Generation (RAG) anchors responses in verified corporate sources – a process also known as ‘grounding’: the agent responds on the basis of the knowledge base.
- Integrations and MCP give agents access to systems such as CRM, ERP, online shops or ticketing systems. MCP is an open standard through which AI systems communicate in a structured manner with external tools and data sources.
- Guardrails define what an agent is and is not permitted to do: which sources it uses, which topics it avoids, and which actions require approval.
- Human takeover ensures that complex or sensitive cases are handed over to staff with the full context.
Areas of application: Where Agentic AI is used within a company
Agentic AI has the greatest impact in customer service, because many enquiries involve standardised processes that, until now, have nevertheless been handled manually:
- Check order status and track parcels in real time
- Change addresses, orders or contracts
- Create returns and initiate refunds
- Book, reschedule or cancel appointments
In sales and e-commerce, agent-based AI is primarily used for personalised advice and intelligent sales:
- Product advice: The agent identifies the customer’s needs, matches them to the product range and recommends suitable products.
- Lead qualification: Enquiries are assessed, enriched and transferred directly to the CRM.
- Appointment booking: Qualified leads are immediately booked in for a consultation appointment in your sales team’s calendar.
In marketing, AI is used to replace forms with dialogue-based data collection and to channel campaign traffic effectively into conversations and conversions. Visitors are addressed in a context-appropriate manner and using the right tone.
Its true strength becomes apparent when specialised agents work together throughout the customer journey: one agent provides pre-purchase advice, another assists with the purchase, and a third handles post-sales support. The context is maintained throughout all stages, and customers do not have to explain anything twice.
Why Agentic AI is the deciding factor in competitiveness by 2026
Customer expectations have shifted, as they now want their enquiries resolved immediately, regardless of the time of day or the channel used. This also changes the key performance indicator: rather than response time, what counts is time-to-resolution – that is, the time it takes for an enquiry to be fully resolved. Companies that successfully utilise Agentic AI gain three advantages as a result:
- Scaling without a linearly growing team: Seasonal peaks and rising volumes of enquiries can be managed without a corresponding increase in staffing requirements.
- Service and sales converge: The same platform resolves support issues, provides pre-sales advice and qualifies leads.
- More time for what people do best: Teams focus on consultation-intensive, complex and emotionally charged issues.
A competitive edge is determined by the quality of implementation. Yet this is precisely where many projects fail: high implementation and maintenance costs, unclear business value or inadequate risk controls are classic hurdles to AI adoption. Added to this is the regulatory framework: the EU AI Act requires companies to take different measures depending on the risk class of their AI systems; in customer-facing scenarios, for example, they must make it clear that users are interacting with an AI (read more in our article on the EU AI Act).
The three most common obstacles and how to overcome them:
Obstacle 1: Loss of control
Agentic AI in a business context does not mean allowing AI to act without supervision, but rather giving it a clearly defined scope of action. Guardrails, source control and escalation rules should be part of the architecture from the outset, rather than being added as an afterthought. This allows you to define the tone and permitted actions, whilst monitoring ensures that every interaction is traceable and can be analysed. Sensitive actions, such as refunds above a certain amount, require human approval. We’ve summarised everything you need to know in our article on guardrails.
Obstacle 2: Lack of acceptance within the team
As AI solutions become more widespread, many staff members are increasingly concerned that jobs will be lost. You should therefore make it clear from the outset what the use case is for introducing agentic AI and what the implications will be. Agentic AI primarily takes on repetitive tasks, so that your team has more time for complex, consultancy-intensive cases. The principle is ‘humans and AI’, not ‘humans versus AI’.
Obstacle 3: Data quality and fragmented knowledge
Many companies underestimate the amount of work involved in preparing the knowledge base before an Agentic AI system can make reliable decisions across all channels. Inconsistent or out-of-date information in the CRM, product database or support documentation is one of the most common reasons why automation rates remain low during the pilot phase.
6 criteria: How to recognise a genuine Agentic AI platform
The following criteria will help you to compare providers in a structured way and to identify whether a solution offers genuine agent capabilities or whether it is a case of so-called ‘agent washing’ (see Gartner). This refers to the practice of labelling simple chatbots or automation tools as AI agents without them actually possessing the relevant capabilities. Which criteria are most important to you will depend on your use cases, channels and system landscape.
- Degree of genuine agentic capability: Can the system merely generate responses, or can it also understand objectives, plan steps and execute actions in backend systems? This is the key difference between ‘agent washing’ and genuine agentic AI.
- End-to-end automation: How many enquiries are actually resolved in full? Simply forwarding enquiries or answering FAQs is not enough.
- Integration with existing systems: An agent is only as good as its integration. Ready-made integrations for CRM and ticketing systems, flexible webhooks and APIs, as well as support for open standards such as MCP, are essential. No-code integrations, such as those offered by moinAI, reduce development effort.
MCP at moinAI
moinAI utilizes MCP both for enabling AI agents to access external systems and for connecting external AI assistants to moinAI. Via the moinAI MCP server, tools like Claude can, for example, access the Knowledge Base, manage content, configure AI agents, and test conversations.
More about moinAI technology- GDPR and governance: Hosting and processing should, as a standard, take place in Europe, and data protection requirements must be met. Comprehensive control over autonomous agents is essential, as Agentic AI cannot be used securely in an enterprise context without guardrails and an authorisation framework.
- Enterprise readiness and scalability: Can the AI be scaled for the desired use case within the organisation? Reliability, monitoring, testing and version management are crucial for a suitable agent-based solution.
- Quality of human takeover: A good system recognises when it has reached an impasse and seamlessly hands over the context, conversation history and information already gathered to a human agent.
The roadmap: Implementing Agentic AI within your organisation
Phase 1: Identifying use cases
Analyse your enquiries: Which issues occur frequently and follow a clear process? Typical examples include order status enquiries, appointment bookings, address changes or lead qualification. Start with two or three use cases that involve high volumes and manageable risk.
Phase 2: Setting objectives and KPIs
What exactly is the AI supposed to achieve along the customer journey: more qualified leads, shorter resolution times, or a lower volume of service enquiries? Define in advance how you will measure success – for example, by the automation rate, time-to-resolution, customer satisfaction (CSAT) or conversion rate.
Phase 3: Consolidating the knowledge base
Before the AI goes live, content from product pages, support documentation, FAQs and internal systems must be consolidated in one place and kept up to date. You should also clarify data protection and compliance requirements and specify which systems the agent should integrate with. Set out clear guidelines on which issues the agent is authorised to resolve independently and when a case should be handed over to a human agent.
Phase 4: Piloting and testing
Deploy the first agents in a limited scope, such as on a specific page or for a particular channel. The approach: start small, learn quickly. A basic agent for the most common enquiries is often ready for testing after just a few days. For moinAI customers, the time to first go-live is typically around four weeks; further roll-outs, for example to subsidiaries, are completed much more quickly.
Phase 5: Scaling
Automation rates typically rise over a period of months. Once the initial use cases are running smoothly, you can expand to cover further processes and additional channels, as well as set up specialised agents. The automatic detection of knowledge gaps (via the ‘Dreaming’ feature in moinAI) reduces the amount of manual maintenance required.
Practical examples: How automation rates are developing
A look at real-life moinAI client projects shows how automation rates develop over time. The common thread: none of these figures was achieved on day one, but rather through continuous development and optimisation:
- ImmoScout24: The automation rate has risen from around 20 per cent to approximately 75 per cent in the space of about two years, with around 300,000 conversations per year. Long-term target: over 90 per cent. → Read Case Study
- Teleboy: Starting at around 53 per cent, the average automation rate now stands at 75 per cent. In 2024 alone, this saved around 2,588 support hours, or 288 working days. → Read Case Study
- Wollenhaupt: Since going live in early 2026, the automation rate has already reached 78 per cent, with less than three hours of maintenance required per week. → Read Case Study
"We maintain the knowledge base ourselves without developers in between, and dialogues can be fine-tuned using real data rather than gut feeling. Handover to the team works seamlessly whenever the bot hits its limits. For me, that’s the most critical point because it’s where most bots fail."
on moinAI's solutions
The Dos and Don’ts of AI Adoption
What works:
- Involve them early on: Engage staff from marketing, sales and customer service right from the start to foster acceptance and understanding.
- Start with a focused approach: Begin with a clearly defined use case, rather than automating the entire journey straight away.
- Set guardrails first: Define guardrails and escalation procedures from day one, not after the fact.
- Make successes visible: Measure progress using specific KPIs and communicate this internally.
What leads to failure:
- Starting too big: If you try to automate everything at once, you’ll lose track of things.
- Neglecting the knowledge base: Outdated content leads to incorrect answers.
- Failing to hand over to humans: Customers get stuck in loops, and frustration mounts.
- Autonomy without control: Without clear guardrails, agentic AI becomes a risk rather than an advantage.
Conclusion
AI adoption is not achieved through the ‘smartest’ AI, but through a controlled, step-by-step roll-out: clear objectives, a solid knowledge base, defined guardrails, deep system integration and active change management. The competitive advantage arises when companies no longer merely respond to issues, but solve them whilst retaining control at all times.
moinAI is the AI platform for customer service, marketing and sales, developed and hosted in Germany. With over 11 years’ experience in AI and more than 170 clients, we help businesses to use Agentic AI safely and in a controlled manner.
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