{"id":66882,"date":"2026-04-13T15:14:16","date_gmt":"2026-04-13T14:14:16","guid":{"rendered":"https:\/\/sovanta.com\/?p=66882"},"modified":"2026-04-21T09:34:01","modified_gmt":"2026-04-21T08:34:01","slug":"whats-the-concept-behind-agents","status":"publish","type":"post","link":"https:\/\/sovanta.com\/en\/whats-the-concept-behind-agents\/","title":{"rendered":"What\u2019s the Concept Behind Agents?"},"content":{"rendered":"\n

AI agents have taken the tech world by storm. Over the past year, systems that can plan tasks, use tools, and autonomously execute workflows have evolved from research demos into everyday products. Some of the most visible examples can be found in software development. In this blog post series, Niklas Fr\u00fchauf<\/a>, Senior Data Scientist at sovanta, explores whether agents are already transforming business processes – or if they are still largely a promise waiting to materialize. To kick things off, he explains the fundamental concepts behind AI agents.<\/strong><\/p>\n\n\n\n

The rise of \u201cvibe coding\u201d describes a workflow where developers describe what they want and AI generates and iterates on the code. Tools like Cursor IDE, Claude Code from Anthropic, or OpenCode illustrate how quickly agentic systems are changing programming workflows. A second wave of adoption has emerged around personal assistants. AI systems (such as ChatGPT, Claude, OpenClaw and others) increasingly act as task managers, research assistants, and workflow helpers capable of coordinating multiple tools and services.<\/p>\n\n\n\n

The excitement has fueled bold predictions about the future of work. Studies from organizations like McKinsey & Company and the World Economic Forum estimate that between 15\u201335% of work activities could eventually be automated by AI technologies, while hundreds of millions of jobs may be reshaped over the coming decade. (Source: www.tiaa.org<\/a> \/ www.mckinsey.com<\/a>) <\/p>\n\n\n\n

Yet outside coding environments and consumer assistants, the picture is less clear. While agentic AI dominates headlines and product launches, enterprise adoption beyond pilots and prototypes appears to lag behind the hype, raising an important question: are AI agents already transforming business processes, or are they still largely a promise waiting to materialize? And more importantly: how can SAP customers already profit from AI agents right now?<\/strong> Let’s start with the concept behind agents.<\/p>\n\n\n\n

What are AI Agents, exactly?<\/h2>\n\n\n\n

Before answering that question, it helps to take a step back and clarify what we actually mean when talking about \u201cAI agents\u201d. The term is often used loosely to describe very different types of systems that operate at different levels of autonomy.<\/p>\n\n\n\n

A useful way to understand the evolution is to distinguish between three levels of AI-powered applications<\/strong>: simple generative AI, tool-using systems, and autonomous agents.<\/p>\n\n\n\n

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  1. Generative AI applications<\/strong>
    The simplest form uses a large language model purely for content generation: you give it a prompt and it produces text, code, or images. Imagine asking an AI to invent a new pasta recipe. The model might generate something creative – but it could also produce something unrealistic or terrible, because it is essentially improvising based on patterns in its training data. This makes it powerful for brainstorming or drafting content, but it has no grounding in external information.<\/li>\n\n\n\n
  2. Tool-using AI (\u201cchains\u201d or agentic workflows)<\/strong>
    The next step adds tool or function calling<\/strong>, allowing the AI to access external systems such as databases, APIs, or search engines. In the cooking example, instead of inventing a recipe from scratch, the AI could search a recipe site such as BBC\u2019s food database, retrieve the top three pasta recipes, and summarize them. These workflows are often called AI chains<\/strong> or agentic pipelines<\/strong>. However, the sequence of steps is still defined by a developer rather than decided autonomously by the model.<\/li>\n\n\n\n
  3. Autonomous AI agents<\/strong>
    The most advanced level introduces planning and autonomy<\/strong>. Instead of executing a fixed workflow, the system can decide which actions to take, use multiple tools, and iterate until a goal is achieved.Returning to the cooking analogy, an AI agent could check what ingredients are in your fridge, ask about dietary preferences, search for recipes, order missing ingredients, and guide the cooking process. The key difference is that the system plans and adapts its actions dynamically<\/strong> rather than following a predefined chain.<\/li>\n<\/ol>\n\n\n\n

    In practice, most AI agents used in enterprise environments today sit somewhere between the second and third category: structured workflows with limited autonomy<\/strong>, rather than fully independent digital coworkers.<\/p>\n\n\n\n\n

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    AI Discovery<\/h3>\n

    Artificial Intelligence (AI) is no longer just a buzzword. Rapid advances in machine learning, generative AI (GenAI), and automated agent systems are transforming industries, reshaping business models and processes, and impacting customer interactions in nearly every sector.<\/p>\n

    \n read more<\/a>\n <\/div>\n <\/div>\n <\/div>\n
    \n \"Von <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n\n\n\n

    Use the simplest approach that solves the business problem<\/h2>\n\n\n\n

    An important observation is that as the autonomy and capabilities<\/strong> of these approaches increase, so do their complexity, token consumption, development effort, and audit requirements<\/strong>.<\/p>\n\n\n\n

    The key takeaway: use the simplest approach that solves the business problem.<\/strong> Not every use case requires a fully autonomous agent.<\/p>\n\n\n\n