Agentic AI
What is the next level of AI?
The AI world is currently moving from “AI responds” to “AI acts”. This is exactly where Agentic AI comes into play: AI agents that not only describe tasks, but also plan and execute steps.
The short answer:
Agentic AI are AI agents that pursue goals, prepare decisions and trigger actions – within clear rules. This is exciting for companies when processes are currently losing time because people are constantly jumping between tools. The catch: without clear boundaries, data and control, this quickly turns into chaos.
The difference is simple: until now, AI has mainly provided answers (text, summaries, ideas). Agentic AI goes one step further and takes over processes, i.e. several small steps in a row, across systems.
This is precisely the area in which companies lose the most time today: People jump between email, ticket system, CRM, Excel, DMS, Teams. Not because they are “slow”, but because the process consists of many micro-steps: Search for information, check, enter, make queries, update status, trigger next action.
An AI agent can take over such chains, but only if you give it clear boundaries: What is the goal? What can happen automatically? Where do we need approval? This is precisely where the most important takeaways in the next section come from.
5 key takeaways
Agentic AI is exciting when AI is not just supposed to deliver texts, but can take over entire workflows “step by step”.
The greatest leverage is often not in the model, but in the process: clear workflows, clear responsibilities and clear rules beat tool hype.
Start small: one process, one goal, one measurable benefit – and only scale up after a stable pilot.
The more autonomous the agent, the more important control and security become: approvals, logging and a stop button are mandatory, not optional.
Agentic AI only has a lasting effect if teams are involved: Training, standards and a simple routine in everyday life prevent it from remaining just “tests”.
What is Agentic AI in one sentence?
Agentic AI means: AI that performs tasks independently and does not just generate texts.
An agent understands a goal, plans intermediate steps, uses tools (e.g. CRM, ticket system, database) and returns a result. You can think of it as a digital “employee”, but only for clearly defined tasks.
Important: Autonomy does not mean “AI is allowed to do everything”. In practice, limits are almost always needed: Approvals, logging, rules for data access and clear responsibilities.
How does Agentic AI differ from ChatGPT and RPA?
ChatGPT (GenAI) generates content. Agentic AI executes tasks. RPA clicks off rules.
It sounds similar, but there is a big difference in everyday life.
Generative AI (e.g. ChatGPT): writes, explains, structures, but it doesn’t “do” anything in the system unless you connect it to tools.
RPA: automates fixed processes as long as nothing unexpected happens.
Agentic AI: can control processes more flexibly, adapt intermediate steps and continue working with new information as long as you set frameworks and rules.
If you already use RPA: Agentic AI is often interesting when processes have too many exceptions and contain human “thinking work” (e.g. prioritizing, merging, making queries).
How does an AI agent work technically?
An AI agent is essentially a loop of: Goal → Plan → Action → Check → Next step.
For this to work, it usually needs four building blocks:
A “brain” (often a language model) that understands and plans.
Tools (APIs/tools) to do something: search, write, work in systems.
Memory/context (e.g. important information about the customer case, rules, history).
Guard rails: What is the agent allowed to do? What must be released? What is logged?
The most important point: it is not the technology that determines success, but the process design. An agent without clear boundaries is like an intern without a briefing.
You don't need a huge start.
Only the right one.
A quick check is often enough to determine the direction.
Which use cases are realistic for companies today?
Use cases in which agents collect and prepare information and trigger defined actions – with clear boundaries – are realistic.
Good starting fields are often:
Customer service/tickets: pre-sort, prepare answers, ask for missing information
Sales/back office: draft quotations, summaries, CRM maintenance with approval
IT operations: solving standard cases, updating documentation, preparing escalations
Knowledge work: internal research, policy/document drafts, creating checklists
Mini check for a sensible start:
If your process is already clear, data is available and success can be measured, the chances are good. If the process is already unclear, you should start there first.
What are the specific benefits of Agentic AI and how do you measure them?
Agentic AI is worthwhile if you want to save time, stabilize quality or reduce risks – measurably in everyday life.
Typical measured variables (without inventing numbers):
Time: Throughput time per process, “time to first draft”, processing time
Quality: error rate, rework, escalations, complaints
Service: response time, initial resolution rate, satisfaction
Risk: fewer incorrect approvals, better documentation, fewer data mishaps (if implemented correctly)
Important: Set a baseline before you start. Otherwise it remains a gut feeling.
Fewer discussions.
More implementation.
We bring in structure and start with the most sensible step.
What is "Service-as-a-Software" and why is it relevant?
The idea: software is no longer operated – it takes care of the service itself.
So not “provide tool”, but “deliver task”. This can have a real impact on business models.
Examples (as a concept, not as a promise):
An agent takes on recurring service cases, creates standard offers or processes simple transactions. Whether this makes sense in your industry depends on rules, liability and data access.
If you are seriously considering this, it belongs in a clean strategy and governance (see also: AI strategy & governance).
What risks do you need to clarify before use?
The main dangers are: wrong actions, lack of control, data leakage and unclear responsibility.
In practical terms, this means
Control: What is the agent allowed to do? What needs approval?
Quality: How do you recognize errors quickly? Who stops the agent?
Data: What data does the agent use – and is it permitted?
Security: How do you prevent manipulation, prompt leaks and access misuse?
People: Who is responsible if something goes wrong?
If you are looking for a clear checklist: AI Tools Compliance fits well thematically.
What does this mean for data protection, security and compliance?
The more autonomous an agent, the earlier data protection and security must be clarified.
You should at least clarify before the pilot:
What types of data are affected (personal, confidential, internal)?
Who can access it – and how is it logged?
Which external services/models are used?
What happens in the event of errors (stop, rollback, incident process)?
What roles are needed internally (and who decides)?
Without clear responsibility, Agentic AI quickly becomes a pilot collection.
You need at least:
a professional person who is responsible for the process and success
IT/security for access, logging, approvals
Data protection/legal, if personal data is involved
one person who holds the big picture (strategy/owner)
If you want to build up skills in parallel: AI training 2026
What tools/frameworks are available - and what really matters?
The rapid advances in AI research and development form the basis for the emergence and spread of agentic AI. The focus is particularly on modern frameworks and architectures that make it possible to efficiently develop and orchestrate autonomous AI agents and integrate them into existing systems.
Important frameworks and platforms for Agentic AI:
- Microsoft AutoGen: Supports the orchestration of multi-agent systems and enables the division of labor among multiple agents across APIs and external tools.
- LangChain: Allows the linking of prompts, tools and memories to complex workflows based on LLMs. Modular, flexible and customizable.
- LangGraph: Uses graph structures to implement stateful processes – particularly useful in regulated industries such as healthcare or logistics.
- Microsoft Semantic Kernel: Focuses on semantic understanding and context-based reasoning. Particularly suitable for interactive applications with high contextual relevance.
This ecosystem is complemented by platforms such as Azure AI Agent Service, UiPath Agent Builder, Google’s Jules and open source initiatives such as Salesforce’s AgentLite.
This diversity shows: The technological basis for Agentic AI is maturing rapidly and is increasingly accessible – not only for tech giants, but also for start-ups and SMEs.
Innovation through multimodal capabilities:
Modern agents not only process text, but also images, audio and video. The integration of multimodal data sources enables a deeper understanding of complex environments – an important step towards true autonomy.
AI works when someone decides
In 30 minutes we clarify: Goals, quick wins, risks, next steps. Without buzzwords.
Conclusion: When is Agentic AI worthwhile - and when is it not?
Agentic AI is worthwhile if you have recurring processes that require a lot of coordination today – and if you have a clean solution for control.
If you have neither access to data nor clear rules, the best first step is often not to “build an agent”, but to lay the foundations: Data, responsibilities, security rules and a realistic start process.
If you would like to check whether Agentic AI is a sensible and safe start in your environment: a brief check or workshop often helps to determine the right entry point.
- Oliver Breucker
- March 5, 2026
FAQ
No. Chatbots respond. Agentic AI can also plan steps and trigger actions.
No. A sensible start is small: a process, clear boundaries, measurable benefits.
Unclear responsibilities and lack of guidelines (who is allowed to do what, who checks what).
Enough for the agent to prepare decisions – and clean enough for you to recognize errors. If data is unclear: clarify data access first.
With approvals for critical steps, logging, monitoring and clear stop rules.
Yes, if interfaces/access are properly regulated. Otherwise it remains “answer” instead of “act”.
As soon as personal data is involved, the purpose, access, storage and service provider must be clearly clarified.
Depends on the use case. Classify early and document properly.
With baseline comparison: time, quality, escalations, throughput times – before/after.
One with high volume, clear steps and clear quality measurement (e.g. ticket triage, offer preparation, document process).
When tool selection, data flows and governance have to be decided at the same time and internal roles are not yet clear. A short, interdisciplinary workshop often saves more time than it costs.
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