What is Agentic AI and how does it work?

This article is the first part of our three-part series on the topic of Agentic AI. The ​goal​ is to provide a solid foundational understanding of agent-based AI systems​,​​ serving ​as a basis for decision-making ​regarding​ future investments and organizational developments. Instead of focusing on technical details, the emphasis is on comprehensible orientation questions, highlighting their relevance ​to​ business decisions.

As part of this series, you can expect the following articles, which will be published in succession:

1. What is Agentic AI and how does it work? (this article)

2. How do you actually use Agentic AI?

3. What use cases are there for Agentic AI?

Agentic AI refers to AI systems that function as autonomous digital agents. ​Unlike​ traditional, rule-based or instruction-driven systems, these agents independently pursue a goal, observe their context, evaluate options for action, and make their own decisions. They operate based on data, contextual knowledge, and experience, aiming not just to react but to act with foresight.

These systems are not confined to a single model, but orchestrate various tools, APIs, and decision logics to accomplish their tasks. Humans define the objective​s​ and the framework for action​, while​ the Agentic AI systems handle the operational execution. This ability to delegate the pursuit of objectives makes Agentic AI a strategic concept for modern enterprise architectures.

Traditional AI works reactively: it analyses data, recognizes patterns, and provides predictions or recommendations based on this. However, it remains dependent on input and predefined processes.

Agentic AI, on the other hand, actively pursues a goal. It plans, acts and learns continuously. This means that an agent can independently decide when an analysis is required, whether additional information is needed, or whether an interaction should be triggered, without each step being predefined by a human.

This represents a paradigm shift for companies: moving away from selective intelligence (e.g. "which customer is canceling?") towards continuous action (e.g. "how can I proactively strengthen customer loyalty?").

Several developments are currently increasing both the pressure and feasibility of Agentic AI:

• Progress on the model side: Foundation models such as LLMs or cross-domain language AI provide the semantic and contextual basis that makes agent-like interactions broadly usable for the first time.
• Economic necessity: Companies need to automate their processes more, while also making them more flexible and resilient. Traditional automation is reaching its limits here.
• Technological integration: APIs, cloud services, data platforms and CRM systems are now easier to connect across the board​,​ allowing Agentic AI systems ​to​ use information across systems.
• Advanced reasoning capabilities: As seen in current language models, like ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google DeepMind) or LLaMA (Large Language Model Meta AI), modern agent systems are increasingly capable of analyzing complex relationships, drawing logical conclusions and deriv​ing​ actionable measures. This ability goes far beyond the classic question-answer principle and forms the basis for the actual automation of business-critical decision-making processes.
• Changed expectation management on the user side: Business units and end users are increasingly demanding systems that not only provide support, but also act proactively and take responsibility, for example​,​ when prioritizing tasks, escalating risks or generating well-founded decision options. For the first time, Agentic AI offers a realistic way ​to​ technically fulfill this requirement.
• Availability of plug-and-play modules: The spread of Low-Code environments, API connectors and standardized toolchains makes the implementation of agent solutions easier, faster and economically viable. This also lowers the barrier to entry for companies without their own AI research.

Agentic AI is therefore not only technologically possible, ​but ​it has also become economically and organizationally viable.

Agentic AI systems are based on an iterative cycle:

1. Perception: The system collects data on statuses, events, user behavior or both structured and unstructured system responses. Depending on the use case, this information comes from various sources, such as ERP systems, CRM platforms, APIs, text documents or sensor technology.

2. Analyze and decide: Based on objectives, company context and existing experience, the system plans possible options for action, prioritizes them​,​ and decides which measure makes the most sense in the respective context. Uncertainties, conflicting objectives and probabilities can also be considered.

3. Act: The selected action is carried out automatically through​ ​technical interfaces. This can be a database entry, a message to a specialist, a system booking or automated further processing in a downstream system.

4. Reflect and learn: The system analyzes the effects of its decision, learns from feedback​,​ and adapts its decision-making logic for future cases - continuously and based on data.

This cycle is repeated continuously, ideally with traceable intermediate steps, so that human users can monitor and fine-tune the systems but do not have to control each step individually.

What is particularly relevant here is that the systems are increasingly able to ​dynamically ​interpret their context of action. They evaluate deviations, recognize missed targets​,​ or ambiguity in the input​,​ and react adaptively accordingly. This enables the transition from static automation to adaptive, context-sensitive action control.

Agents can be divided into maturity levels or functional characteristics. The following classification helps companies select suitable agent types for different challenges:

• Reflex agents react immediately to triggers according to fixed rules, ​similar​​ t​o classic RPA (Robotic Process Automation) solutions. They work deterministically, quickly and reliably, but are only suitable for clearly structured, recurring processes without freedom of choice.
• Model-based agents understand internal relationships and can think in context. They build up an internal image of their environment to better assess and anticipate systemic changes. They are used, for example, in process forecasting, warehouse optimization​,​ or dynamic production control.
• Goal-oriented agents plan strategically how to achieve a defined goal efficiently. They evaluate paths of action according to goal achievement and decide on the most promising path, which is ideal for resource allocation, sales management​,​ or automated project planning.
• Benefit-oriented agents make decisions based on an evaluated target system (e.g., cost-benefit assessment, customer satisfaction, time savings). Such agents are often used in complex scenarios in which several interests must be considered simultaneously, e.g.​,​ in logistics optimization or for pricing strategies.
• Agents capable of learning develop continuously through data feedback. For example, they use reinforcement learning or continuous optimization approaches to learn with every decision. They are particularly relevant for repetitive processes with high data availability, such as anomaly detection or customer experience management.
• Hierarchical agent systems (also known as agent networks or orchestrated agent networks) consist of several agents organized in an overarching architecture. They divide up tasks, communicate with each other​,​ and jointly control complex overall processes, for example​,​ in production chains, digital twins or company-wide service platforms.

In practice, there are often smooth transitions between these types. A well thought-out start with clearly defined goals, a suitable use case​,​ and a focused scope are crucial for successful projects.

Agentic AI is not aimed at selective automation but at the structural improvement of decision-making and control processes. In a business context, it ​scores​ particularly well in​ ​the following areas:

• Autonomy thanks to the reduction in operational workload for teams. Agent systems can independently take on tasks, make decisions​,​ and initiate follow-up actions, for example​,​ in customer communication, quotation processes​,​ or the coordination of internal systems. They act within the framework of defined guidelines and help to enable scalability without loss of control.
• Decision-making capability thanks to speed, transparency and consistency in the decision-making process. Agentic AI systems can map complex decision trees in real time, merge rule-based and data-based criteria, and thus suggest or directly implement well-founded courses of action – which is ideal for risky, time-critical or data-intensive areas of application.
• Ability to learn thanks to continuous further development based on feedback, user interactions and operational results. This prevents systems from remaining static or becoming obsolete due to manual maintenance. The systems optimize themselves according to defined success metrics, whether to reduce costs, improve throughput times​,​ or increase quality.

Agentic AI is therefore becoming a key component of digital resilience, especially in data-intensive, complex and decentralized environments.

Many companies are already working with RPA, BPM or rule-based engines. These tools have proven their worth, but reach their limits where processes are unpredictable, dynamic or cannot be fully defined.

Agentic AI systems provide four decisive improvements here:

• Understanding context: Traditional automation requires structured data and fixed rules. Agentic AI systems, on the other hand, ​can​ process semi-structured or incomplete information, recognize correlations​,​ and make decisions based on this. This increases their applicability in realistic, heterogeneous process landscapes.
• Decision autonomy: While RPA processes function according to the principle "If A, then B", agent systems evaluate several options for action simultaneously, taking into account conflicting goals, framework conditions and effects. This allows differentiated, risk-based​,​ or benefit-maximizing control even under uncertain conditions.
• Target adjustment: Conventional workflows are rigid. Agentic AI systems are dynamically oriented towards higher-level goals and adapt their path flexibly, for example​,​ in the event of resource bottlenecks, changes in the customer signal​,​ or changing priorities.
• Scalable intelligence layer: Agents can extend an existing automation logic by being used as a decision precursor or meta-controller. This creates an intelligent addition instead of a complete replacement with high connectivity to existing platforms.

This means that Agentic AI does not replace traditional tools but complements them with a new, adaptive control and decision-making level that expands and modernizes existing automation solutions in a meaningful way.

Agentic AI is on the cusp of widespread use. Companies that acquire an understanding of its principles, potential applications​,​ and requirements today will secure strategic advantages for the next wave of digital transformation. At the same time, it is important to manage expectations realistically. Agentic AI is not a jack of all trades​: it​ cannot replace strategic leadership, empathy​,​ or human judgement in ethically complex situations. However, it can prepare decisions, automate repetitive procedures​,​ and make processes more intelligent. Agentic AI offers potential for controlled, traceable innovation, particularly in the highly regulated Swiss environment, such as in the financial or healthcare sectors. Anyone who not only wants to digitize processes but also future-proof business-critical workflows can reach the next maturity phase of digital value creation with Agentic AI.

Find out more in our webinar

We will cover these and other topics in more detail in our Agentic AI webinar, which will take place in fall 2025. You can find more information about the webinar here and register directly to take part live.

Would you like an in-depth technical discussion with our experts?

We develop individual strategies and solutions that are tailored to your specific requirements. Feel free to contact us directly at data_ai@adesso.ch. We look forward to exchanging ideas with you.