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  "title": "Agentic and generative AI: what is the difference?",
  "hook": "Generative AI produces content, such as a summary of supplier emails. An agentic system can also choose and carry out next steps: opening the order and finding a missing delivery confirmation, for example. It may use the same generative model. For your team, the practical question is which work the AI may take on and who checks the result. [S1][S5]",
  "summary": "Understand agentic AI vs generative AI through one delivery-status task: model capability, tool use, permissions and human responsibility.",
  "pillar": "Hybrid Organisation",
  "author": {
    "name": "Marc Seefelder",
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  "datePublished": "2026-10-12",
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      "id": "S1",
      "title": "Agentic AI vs. generative AI: What’s the difference and how they work together",
      "publisher": "SAP",
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      "url": "https://www.sap.com/resources/agentic-ai-vs-generative-ai-what-s-the-difference-and-how-they-work-together",
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      "title": "Building effective agents",
      "publisher": "Anthropic",
      "date": "2024-12-19",
      "url": "https://www.anthropic.com/engineering/building-effective-agents",
      "supports": [
        "Predefined workflows, dynamic steps and human checkpoints"
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      "title": "Agents overview",
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      "date": "2026-10-01",
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        "Chosen example, permissions, approval and completion evidence; no measured results"
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      "id": "S5",
      "title": "Was ist eine agentische KI?",
      "publisher": "Google Cloud",
      "date": "Accessed 2026-10-12",
      "url": "https://cloud.google.com/discover/what-is-agentic-ai?hl=de",
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        "Content generation and goal-directed action; generative models within agentic systems"
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      "q": "Does agentic AI replace generative AI?",
      "a": "No. These terms describe different layers: generating content is a model capability, while agentic behaviour depends on how a system uses models, tools and feedback. [S1]"
    },
    {
      "q": "Is a Copilot or an assistant necessarily an agent?",
      "a": "The name alone does not tell you how it operates. Microsoft’s Copilot Studio overview includes different approaches, from rule-based flows to reasoning. Inspect the actual actions and approval rules. [S3]"
    },
    {
      "q": "Can an agent still require human approval?",
      "a": "Yes. Human checkpoints can be part of an agentic system. In our illustrative example, an employee approves a record change; the agent still chooses how to investigate the missing delivery status. [S2][S4]"
    }
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  "contentMarkdown": "---\ntitle: \"Agentic and generative AI: what is the difference? — MING Labs\"\ndescription: \"Generative AI produces content, such as a summary of supplier emails. An agentic system can also choose and carry out next steps: opening the order and findin\"\ncanonical: https://www.minglabs.com/insights/concepts/agentic-ai-vs-generative-ai\nlang: en\nlast-updated: 2026-10-12\n---\n\n# Agentic and generative AI: what is the difference?\n\n[Home](/)  [Insights](/insights)  [Concepts](/insights/concepts)  Agentic and generative AI: what is the difference?\n\n[English](/insights/concepts/agentic-ai-vs-generative-ai)[Deutsch](/de/insights/concepts/agentische-ki-vs-generative-ki)\n\nConcept\n\n\n\nGenerative AI produces content, such as a summary of supplier emails. An agentic system can also choose and carry out next steps: opening the order and finding a missing delivery confirmation, for example. It may use the same generative model. For your team, the practical question is which work the AI may take on and who checks the result. \\[S1\\]\\[S5\\]\n\nLast updated: 12 October 2026 | Next review: November 2026 Sources and illustrative MING method [Machine-readable record ](/insights/concepts/agentic-ai-vs-generative-ai.json)\n\nMarc Seefelder · Founding Partner, MING Labs\n\nIllustrative example: will the delivery arrive on Friday?\n\nWho finds the missing delivery confirmation?\n\nThe same generative AI model can write in either setup.\n\nAI as a writing assistant\n\n1.  Employee provides the available messages.\n2.  AI summarises: “Friday is not yet confirmed.”\n3.  Employee finds the confirmation and checks the date.\n\nThe person organises the investigation.\n\nAI as an agent\n\n1.  Employee asks it to check the delivery date.\n2.  Agent reads the order and looks for the missing confirmation in approved records.\n3.  Agent checks the evidence and presents the supported date.\n\nThe agent chooses its next step based on what it finds. If evidence is missing, it asks the employee.\n\nIn this example, a person records the confirmed date or approves the change.\n\nTwo chosen workflows, not a performance test. “Generative” describes producing content; “agentic” describes choosing work steps within an assignment. A fixed workflow can also use tools. \\[S1\\]\\[S2\\]\\[S4\\]\n\n##  A writing assistant leaves the search to you\n\nTake a delivery query: will the supplier deliver on Friday? In this illustrative example, you give a writing assistant the messages already at hand. It can summarise them and draft a reply. If none confirms Friday, you still need to find the confirmation and record the date. The summary helps with part of the work; the investigation remains yours.[\\[S4\\]](#source-s4)\n\n##  The agent looks for the missing delivery confirmation itself\n\nNow suppose you give an agent the goal and order reference. In our proposed setup, it may read the order and approved supplier records. A missing date leads it to look for a confirmation. If it finds one, it checks the order number and date before proposing an update. Its next step depends on what it finds; you do not have to choose each document yourself.\n\nThat leaves a separate decision: who may change the delivery record? Here, an employee must approve the change. If the confirmation is missing or contradictory, the agent brings the open question back to that person. It may not contact the supplier. As the diagram shows, delegating the search does not also grant permission to send messages or change records.[\\[S4\\]](#source-s4)\n\n##  The agent can use the same generative model\n\nThe summary could come from the same generative model in either setup. The difference lies in the surrounding system: an agentic system can choose a next step, use a permitted tool and check the result. Generating content and directing the work can therefore belong to the same application.[\\[S1\\]](#source-s1)[\\[S2\\]](#source-s2)\n\nTool access alone does not settle the distinction. A fixed workflow can also read records and call a model. Anthropic distinguishes those predefined paths from agents that dynamically direct their steps. For the delivery query, ask whether the search always follows the same path or needs to change with the evidence.[\\[S2\\]](#source-s2)\n\n##  Decide what the agent may read and change\n\nTake one delivery query from your team and write down which records the system may read, who approves a change and what confirms completion. In our example, completion means finding and checking the confirmation for the right order. These boundaries define the role we propose for the agent. Alongside its access to records, it needs a colleague who checks its work and helps when a question remains unresolved. This is what we mean by a [Hybrid Organisation](/hybrid-organisation) .[\\[S4\\]](#source-s4)\n\nUse that task description with our [agent-versus-workflow guide](/insights/answers/when-should-you-use-an-ai-agent-instead-of-workflow-automation) to decide whether it needs dynamic steps. The example is a proposed method, not a client case or evidence of savings.\n\n## Sources\n\n\\[S1\\]\n\n[Agentic AI vs. generative AI: What’s the difference and how they work together](https://www.sap.com/resources/agentic-ai-vs-generative-ai-what-s-the-difference-and-how-they-work-together) SAP · 2026-09-25 Supports: Model capability and system architecture can work together\n\n\\[S2\\]\n\n[Building effective agents](https://www.anthropic.com/engineering/building-effective-agents) Anthropic · 2024-12-19 Supports: Predefined workflows, dynamic steps and human checkpoints\n\n\\[S3\\]\n\n[Agents overview](https://learn.microsoft.com/en-us/microsoft-copilot-studio/agents-overview) Microsoft Learn · 2026-10-01 Supports: Different operating approaches under the same product label\n\n\\[S4\\]\n\nIllustrative delivery-status task and proposed checks MING Labs · 2026-10-12 Supports: Chosen example, permissions, approval and completion evidence; no measured results\n\n\\[S5\\]\n\n[Was ist eine agentische KI?](https://cloud.google.com/discover/what-is-agentic-ai?hl=de) Google Cloud · Accessed 2026-10-12 Supports: Content generation and goal-directed action; generative models within agentic systems\n\n## Frequently asked questions\n\nDoes agentic AI replace generative AI?\n\nNo. These terms describe different layers: generating content is a model capability, while agentic behaviour depends on how a system uses models, tools and feedback. \\[S1\\]\n\nIs a Copilot or an assistant necessarily an agent?\n\nThe name alone does not tell you how it operates. Microsoft’s Copilot Studio overview includes different approaches, from rule-based flows to reasoning. Inspect the actual actions and approval rules. \\[S3\\]\n\nCan an agent still require human approval?\n\nYes. Human checkpoints can be part of an agentic system. In our illustrative example, an employee approves a record change; the agent still chooses how to investigate the missing delivery status. \\[S2\\]\\[S4\\]\n\n## Available actions\n\n- **Copy content for my AI**: copies the [AI briefing](https://www.minglabs.com/insights/concepts/agentic-ai-vs-generative-ai.md) to the clipboard in the browser.\n\n---\n\nCanonical: https://www.minglabs.com/insights/concepts/agentic-ai-vs-generative-ai\nMachine-readable index: https://www.minglabs.com/llms.txt\n"
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