The question of agentic AI vs generative AI comes up the moment someone on your team pastes a ChatGPT answer into an invoice, an email or your CRM by hand. The AI wrote the text in seconds. Then a person spent the next ten minutes clicking it into the right system. That gap, between an answer and a finished task, is exactly where the difference between the two lives.
For owners, team leads and IT in small companies and the Mittelstand, the distinction is not academic. One kind of AI helps you write faster. The other kind does the work. Here is what separates them, where each fits, and how to start without a big project.
Key takeaways
Generative AI creates content from a prompt - text, images, drafts. You ask, it answers, and you do the next step yourself.
Agentic AI pursues a goal - it plans, uses your systems and takes real actions, asking for approval before anything goes out.
They layer, not compete - agentic AI runs on a generative model at its core. Superkind is the agentic example: you write it a task in Outlook, Teams or Slack and it gets done in your programs.
What Is the Difference Between Agentic AI and Generative AI?
Generative AI is software that creates new content - text, images, audio or code - in response to a prompt. Agentic AI is software that pursues a goal by planning steps, using your tools and taking actions across your systems, with a human approving the critical ones. In short: generative AI answers, agentic AI acts.
IBM frames the split cleanly: generative AI “creates new content such as text, images, video, audio and software code”, while agentic AI “focuses on achieving goals by planning, making decisions” and carrying out multi-step tasks1. The key point for a business reader: agentic AI almost always has a generative model at its core. It is not a replacement for generative AI - it is the layer that turns a clever answer into finished work.
The one-line test
Ask yourself who does the next step. If the AI hands you text and you move it into a system, that is generative AI. If the AI moves it into the system itself and asks you to approve, that is agentic AI.
How Superkind Does It: Agentic AI in Daily Work
Superkind is an AI employee for your team. You write to it in Outlook, email, Microsoft Teams, Slack or the web app like a colleague, and it does the task in your existing programs - then asks before anything goes out. That is agentic AI made concrete: not a chat window you copy out of, but a colleague who finishes the job.
- 1
You write the task
Forward an invoice in Outlook or write in Teams or Slack, in plain language, the way you would brief a colleague. No new tool to learn.
- 2
Superkind does it in your systems
It works in the programs you already use - DATEV, SAP Business One, HubSpot, SharePoint, Excel and 1,500+ others - using your company knowledge and your rules, so nobody copies data between systems by hand.
- 3
It asks before anything goes out
Before it sends, books or changes anything that leaves your team, Superkind shows you the result and waits for Approve or Reject. You stay in control.
- 4
It becomes a routine
What you explain once keeps running. Every task makes Superkind smarter, and that knowledge - your customers, prices and processes - stays in the company even when people leave.
Book the invoice from Moeller and match it against the purchase order.
When the work gets more complex, you do not start over. You build custom solutions and apps with their own interfaces on top of what Superkind already knows about your company - yourself or with the Superkind team. Everything belongs to you.
Send Superkind your first task
Start for free in Outlook, Teams or Slack. See agentic AI do the work, not just describe it.
How Agentic AI Works
Generative AI runs a single loop: prompt in, content out. Agentic AI runs a longer one. McKinsey describes it as a system built on generative foundation models that can act in the real world and execute multi-step processes2. In practice it works like this:
- You set the goal - not a prompt for one answer, but an outcome: “book this invoice”, “answer this request”, “send the weekly report every Friday”.
- It plans the steps - the agent breaks the goal into the actions needed: read the document, find the customer, check the order, prepare the entry.
- It uses your tools - it connects to your real systems through integrations and moves data between them, instead of answering in a separate window.
- It acts, with approval - it carries out the steps and pauses at the point where something would leave your company, asking you to confirm.
- It learns from the outcome - your correction or approval becomes part of what it knows, so the next run needs less input.
Why this matters now
Gartner projects that 33 percent of enterprise software applications will include agentic AI by 2028, up from less than 1 percent in 2024, and that at least 15 percent of day-to-day work decisions will be made autonomously by then, up from none in 20244. The shift from answering to acting is the reason.
Agentic AI in Comparison
“AI” is used for four very different things. The table below puts agentic AI next to generative AI, a classic chatbot and RPA (robotic process automation), so you can see where each one stops.
| Aspect | Generative AI | Agentic AI | Chatbot | RPA |
|---|---|---|---|---|
| What it does | Creates content from a prompt | Pursues a goal and takes actions | Answers scripted questions | Repeats fixed, recorded steps |
| Who does the next step | You do | The AI does | You do | The bot does (no judgement) |
| Works in your systems | Only via copy-paste | Yes, across connected systems | Usually one channel | Screen-level only |
| Handles exceptions | Not its job | Adapts and asks when unsure | Fails or escalates | Breaks when the screen changes |
| Approval before it acts | No action to approve | Yes - Approve or Reject | No action to approve | Runs without review |
| Example | Draft a reply to a complaint | Answer the request end to end | FAQ pop-up on a website | Copy fields between two apps |
The honest caveat: agentic AI is harder to get right than a chat assistant, because it acts on live systems. Gartner expects more than 40 percent of agentic AI projects to be canceled by the end of 2027, often because teams buy “agents” that are really rebranded chatbots, or aim for full autonomy where a lighter setup with human approval would do5. Starting with one clear task and a human in the loop is how you stay on the right side of that number.
“AI offers enormous opportunities for companies, regardless of size or industry. The greatest danger is simply ignoring AI and missing the train.”
- Dr. Ralf Wintergerst, President of Bitkom6
Examples: Generative vs Agentic in the Same Task
The clearest way to feel the difference is to take one job and see where each kind of AI stops. Superkind is the agentic example in each case.
- Superkind - booking an invoice - You forward the invoice in Outlook. Superkind reads it, matches it against the purchase order in SAP Business One or DATEV, prepares the entry under your rules and asks you to approve. Generative AI would only tell you how to book it.
- Answering a customer request - A generative assistant drafts a reply you then send. An agent pulls the order status from your system, writes the answer, and after your approval sends it from the right inbox and logs it.
- Comparing quotes - Generative AI summarises two PDFs you paste in. An agent reads the quotes where they arrived, compares them against your criteria, and flags the one over your sign-off threshold for management.
- Qualifying leads - A chat assistant suggests qualifying questions. An agent checks the new lead in HubSpot, enriches it, scores it against your rules and routes it to the right salesperson.
- Writing marketing copy - This is genuinely a generative AI job: a first draft of a post or a product text. Agentic AI is overkill here, and that is fine - not every task needs an agent.
- The weekly report - Generative AI formats numbers you supply. An agent gathers them from your systems every Friday, builds the report and sends it to the right person as a standing routine.
What Does Agentic AI Cost?
Pricing follows what each kind of AI does, so the two are not priced the same way.
- Generative AI assistants - usually sold per user per month, often around 20 to 30 euros for business plans. You pay for access to a chat window and seats scale with headcount.
- Agentic AI - priced by scope, not just seats, because it does real work across your systems. The value is measured in tasks finished and hours saved, not in licences.
- Superkind - there is no public price list. You can start for free and, when you want to see what a rollout in your own systems looks like, book a demo. We never quote a figure here that would not match your setup.
- The hidden cost either way - the real expense is the manual step between answer and system. That is the cost agentic AI removes, and the one a chat window leaves in place.
A note on compliance cost
Agentic AI acts on your data, so where it runs matters. Superkind is hosted and processed in Europe, is GDPR compliant and aligns with the EU AI Act, which becomes fully applicable on 2 August 20267. It does not train models on your data. That keeps compliance a setup detail, not a recurring surprise.
How to Start With Agentic AI
You do not need a strategy deck or an in-house AI team. Start with one task and let it prove itself.
Your first week with agentic AI
- Pick one repetitive task that crosses two systems (invoice booking, standard requests, quote comparison)
- Sign up for free at app.superkind.ai and connect the systems that task touches
- Write the task to Superkind in Outlook, Teams or Slack, in plain language
- Check the result and click Approve - the first time, review everything
- Once it runs reliably, turn it into a routine and add the next task
- Keep generative AI for drafting and research - the two work side by side
Conclusion
Agentic AI vs generative AI is not a contest - it is a division of labour. Generative AI writes the draft; agentic AI finishes the task in your systems and asks before anything goes out. Most companies already use the first kind without realising how much manual work it leaves behind. The move that pays off is handing one of those tasks to an agent.
Pick the task that annoys your team most on a Monday - the one where an AI answer still ends in ten minutes of clicking. Write it to Superkind this week and approve the result. That single task is the whole difference, made real.
Frequently Asked Questions
Generative AI creates content in response to a prompt: text, a draft email, an image, a summary. You ask, it answers, and you do the next step yourself. Agentic AI takes a goal and works through the steps on its own: it plans, uses your systems, and takes actions like booking an invoice or sending a quote. Agentic AI usually has a generative model at its core, so it is less a rival to generative AI than the layer built on top of it.
No. A chat assistant like ChatGPT gives you an answer in a window; you still copy the result into DATEV, Outlook or your CRM yourself. Agentic AI connects to those systems and does the step for you, then asks for approval before anything leaves your company. The difference is action, not just answers.
No. The two work together. Generative AI is the engine that reads, writes and understands language. Agentic AI adds planning, memory, tool access and the ability to act, so it can finish a multi-step task instead of returning a single answer. You still use generative AI for drafting and research.
Well-built agentic AI keeps a human in the loop. Superkind, for example, asks you to Approve or Reject before it sends, books or changes anything that leaves your team. It follows the rules you set, such as quotes over a certain amount needing sign-off, and every action is logged. You stay in control of what actually goes out.
Generative AI assistants are usually priced per user per month, often around 20 to 30 euros. Agentic AI is priced by scope, because it does real work across your systems rather than answering in a window. Superkind has no public price list: you can start for free and book a demo to see what a rollout in your systems would look like.
Pick one repetitive task that crosses two systems, like booking supplier invoices or answering standard requests. Sign up for free, write the task to Superkind in Outlook, Teams or Slack like a colleague, and approve the first result. Once it runs reliably, you turn it into a routine and add the next task.
Most business agents for process automation fall into the lower-risk categories of the EU AI Act, which becomes fully applicable on 2 August 2026, so the obligations are light. What matters is where your data is processed and whether it trains external models. Superkind is hosted and processed in Europe, is GDPR compliant, aligns with the EU AI Act, and does not train models on your data.
Related Articles
- AI Agents for the Mittelstand
- From Tools to Teammates: Why “AI Tool” Is the Wrong Mental Model
- AI for Small Business: Tools, Benefits and Use Cases
- How to Automate Invoice Booking with AI
Sources
- IBM Think - Agentic AI vs Generative AI: What Is the Difference?
- McKinsey - What Is Agentic AI? (Explainer)
- McKinsey - The State of AI in 2025: Agents, Innovation, and Transformation
- Gartner - How Agentic AI Is Shaping Business Decision-Making
- Gartner - Over 40% of Agentic AI Projects Will Be Canceled by End of 2027
- Bitkom - Durchbruch bei Kuenstlicher Intelligenz (Dr. Ralf Wintergerst)
- EU AI Act - Implementation Timeline
Send Superkind your first task.
Start for free in Outlook, Teams or Slack. Or let us show you in 30 minutes how it runs in your systems.