Sovereign AI as a Competitive Advantage: Agentic AI in Customer Service
Agentic AI in customer service refers to AI systems based on large language models that can independently understand goals, control processes and resolve cases end to end – for example authentication, data requests or bookings. They break tasks down into individual steps, make decisions and access backend systems such as ERP, CRM or ticketing along the way.
What Is Really Behind Agentic AI?
Unlike traditional chatbots or rigid IVR menus, agents are not tied to a fixed conversation path. They capture customers’ concerns directly from natural language, take the relevant context into account and make coherent, traceable decisions even across multiple interaction steps. This is precisely what makes them so attractive to service departments struggling with growing request volumes, staff shortages and ever-higher expectations for availability.
It is important to remember, however, that these are probabilistic systems: the underlying model always opts for the statistically most likely next response or action. Think of it as an exceptionally hard-working employee who occasionally lacks common sense. The result can be misjudgements, such as assigning a request to the wrong category, offering inappropriate goodwill gestures or giving ambiguously worded information.
Practical experience suggests that companies benefit most when they do not treat agents as an isolated standalone solution but embed them in a broader automation strategy – one that covers all contact channels, such as phone, email and chat, equally. AI agents only deliver their full value when they are closely integrated with existing systems and can resolve requests independently from start to finish.
When Process-Oriented Automation Beats Agents
Clearly structured standard processes – such as meter reading submissions, bulky waste collection bookings or simple address changes – are typical candidates for process-oriented automation. The workflows are clearly defined: identification, a few mandatory details, confirmation. For scenarios like these, rule-based workflows usually provide the most reliable solution at the lowest risk.
A typical bulky waste booking might look like this: capture the address, suggest three possible dates from the collection schedule, confirm the selection, create the case in the system and send an SMS confirmation. Agentic AI adds little value here but increases complexity and testing effort. Process adherence is almost complete with a defined workflow – any deviations are automatically routed to a human agent as a fallback.
Experience from the waste management and energy sectors shows that such processes achieve very high automation rates even without AI, provided the dialogue flow is clear and the data quality is right. An additional agent may seem “smarter”, but it does not noticeably improve either success rates or customer satisfaction.
Clear process design also makes economic sense: a deterministic flow requires little to no AI resources. While a fully agentic phone use case can quickly consume 100,000–200,000 tokens per call, hybrid or purely process-based approaches often get by with just a few thousand tokens – sometimes without any large language models at all.
Where Agentic AI Shines: Complex Requests and Customer Experience
Agentic AI shows its strengths wherever requests are complex in content, difficult to structure or highly variable. Waste disposal is a good example: customers describe all kinds of items, mix several types of waste in a single request (“old wood, some rubble and a few pizza boxes”) and still expect a clear disposal recommendation.
A purely process-based design would have to map every conceivable combination into rigid decision trees – which is practically impossible. An agent, by contrast, only needs a list of permitted categories and the corresponding rules (such as “greasy pizza boxes go in the general waste”) and can derive suitable answers from them in real time. Errors cannot be ruled out entirely, but the risk remains manageable: in the worst case, incorrect information leads to an unnecessary trip to the recycling centre.
Agents also play to their strengths in goodwill decisions at insurance companies or in complaint management. They can evaluate contract details, case histories and correspondence and derive a concrete decision proposal from them. In a human-in-the-loop model, the agent prepares a structured proposal with its reasoning, along with a draft response, which a responsible employee then reviews and approves. This noticeably reduces the processing effort, while decision-making responsibility remains with a human.
Experience from various projects confirms that a hybrid approach in particular – combining AI support with human oversight – is especially effective.
For anyone who missed it: the I-CEM talk on Agentic AI in Customer Service from the 17th Customer Service Week is available to watch here (in German).
Hybrid Automation in Customer Service: Two Practical Examples
A hybrid approach combines the reliability of clearly defined processes with the flexibility of agentic AI. The key is a deliberate distinction: where is classic process design sufficient, where does an agent create real added value – and where does the human retain decision-making authority?
Example: municipal utility / energy provider. Meter reading submissions run as a robust, rule-based workflow – identification via contract number and date of birth, entry of the meter reading, plausibility check, handover to the ERP system. For more complex matters such as tariff questions, billing logic or unusual consumption spikes, an agent is used instead, with a fallback to the service team when needed.
Example: waste management company. Bulky waste collection booking follows a clear process logic with fixed slot allocation. Questions about the correct disposal of all kinds of items, on the other hand, are handled by an agent that understands free-text input, assigns categories and provides suitable answers. The subsequent confirmation, documentation and analysis are once again handled by the process engine.
In both cases, agents are used specifically where their strengths – language understanding, contextual awareness, decision support – are actually needed. Sensitive or heavily regulated decisions, such as benefit rejections or critical contract changes, deliberately remain within a human-in-the-loop structure with clearly defined responsibilities.
Competitive Advantage Through Smart Combination
Agentic AI is not a replacement for classic process design, but a meaningful complement to it. Companies gain a real competitive advantage where they deliberately combine the two: robust, rule-based workflows for structured standard cases and powerful agents for complex, variable requests, where language understanding and context make the difference.
To keep this hybrid approach from becoming an expensive experiment, three things are needed. First, a sober analysis of your own service processes instead of a technology-driven quick fix. Second, clear guardrails for data location, liability and accountability – especially with regard to European regulations. Third, a deliberate cost-benefit balance, since uncontrolled token consumption can quickly become a cost driver.
Those who proceed step by step – from analysis through focused pilots to a scalable operating model – create a service architecture that meets both today’s efficiency requirements and tomorrow’s customer expectations. This is how agentic AI turns from a hype topic into a genuine strategic advantage in customer service.
