With AI agents for smart automation

Agentic AI describes AI systems that not only respond to individual requests, but also act in a targeted manner: they can plan sub-steps, obtain information, make decisions (e.g. "which system is responsible?"), check interim results and derive the next sensible step from them. This creates a capable assistant that actually completes tasks.

For a precise understanding, a brief categorisation helps: an AI agent is typically a combination of a language model (LLM) plus tools (APIs/workflows), knowledge access (e.g. knowledge base, CRM data), state/memory (for context over several steps) as well as policies and guardrails that define what is permitted and how results are secured. Agentic AI is therefore less about a single model and more about a system design that brings together understanding, planning and execution in a controlled manner.

It is also important to note that "agentic" does not mean "uncontrolled autonomous". In professional environments, an agent works within clear guidelines. These include clearly defined data sources, authorisations and possibly human approvals or human-in-the-loop. It is precisely this controlled environment that makes agentic AI relevant for productive automation.

The key advantage: automation can become more flexible because it is not exclusively dependent on pre-planned paths. Traditional automation is strong where processes are clear and deterministic. In service and support, however, there are often unclear, unstructured requirements: Users describe concerns differently, information is missing, cases vary and context is distributed across multiple systems. This is exactly where AI agents can help: They can deal with natural language, specifically request missing information, merge knowledge from multiple sources and dynamically control the process. The result is then transferred to defined workflows.

This is particularly relevant for knowledge and service processes that require a lot of context, such as support and complaints, lead qualification, appointment and ticket processes, internal knowledge queries or the orchestration of multiple systems (CRM, ERP, knowledge base, ticketing).

Compared to rule-based bots, the difference is noticeable: rule-based systems work like decision trees ("if user says X, answer Y"). This is reliable, but inflexible: even small deviations in formulations or unexpected situations lead to dead ends or escalations. Agentic AI, on the other hand, can adapt to case contexts. Instead of specifying each dialogue path in advance, target states, rules, guardrails and available tools are described - and the agent finds a suitable path within these guidelines.

The difference is most noticeable in the user experience: instead of adapting to the logic of a system ("Please select 1, 2 or 3"), users can formulate their request as they see fit. An agent-based system guides users through the process, asks precise questions, summarises interim results and performs actions in the background. For example, creating or updating a ticket, looking up a contract or delivery status, triggering a callback or updating master data. This feels less like "dialogue with a form" and more like collaboration with a competent counterpart: faster, more natural and with less friction.

At the same time, many solutions currently in use are intermediate stages. Although they already use LLMs to formulate answers more fluently or recognise intents better, they still follow relatively rigid communication patterns and predefined processes. The result is bots that appear noticeably more confident - and in many cases already bring measurable improvements, for example in understanding, tonality and the ability to summarise information. In situations that deviate from the expected process, however, there are still limits: planning over several steps, reliable tool orchestration and clean follow-up in the event of ambiguities are often not yet consistently solved. Against this backdrop, Agentic AI can be seen as the next stage of development.

Agentic AI shifts the focus away from "scripting conversations" and towards the design of capabilities (tools, knowledge, rights, decision-making logic) and quality assurance (evaluation, monitoring, guardrails).

The real leap comes from reliable deployment: an agentic system needs clear responsibilities, verifiable results and controlled escalation if data is missing or risks arise. Clean tool and data access, defined evaluation criteria and monitoring during live operation are therefore crucial. Agentic AI then marks the next evolutionary step: from reactive systems to goal-orientated, actionable assistance systems.