--- slug: "multimodale-agenten-warum-bild-video-und-code-jetzt-in-einem-workflow-landen-ein" title: "Multimodal Agents: Why Image, Video, and Code Are Now Coming Together in One Workflow — Context, Practice, and Implications" date: "2026-05-11T00:00:00.000Z" category: "Analysis" eyebrow: "AI Analysis" excerpt: "Multimodal agents connect image, video, and code into a shared working flow. This article explains where that is useful in practice and which guardrails teams need." readTime: 6 coverImage: "/images/ratgeber/multimodale-agenten-warum-bild-video-und-code-jetzt-in-einem-workflow-landen-ein-cover.webp" secondaryImage: "/images/ratgeber/multimodale-agenten-warum-bild-video-und-code-jetzt-in-einem-workflow-landen-ein-workflow.webp" tags: - "Multimodal" - "AI Agents" - "Workflows" - "Explainer" sidebarTitle: "Bottom line" sidebarPoints: - "The era of purely text-based AI is drawing to a close." - "The key difference from earlier systems lies in the architecture of the underlying models." relatedTools: - {"title":"Claude","href":"/tools/claude/"} - {"title":"GitHub Copilot","href":"/tools/github-copilot/"} - {"title":"Cursor","href":"/tools/cursor/"} - {"title":"Aider","href":"/tools/aider/"} - {"title":"Vyrill","href":"/tools/vyrill/"} - {"title":"Naoma AI","href":"/tools/naoma-ai/"} - {"title":"Hera","href":"/tools/hera/"} - {"title":"LangChain","href":"/tools/langchain/"} - {"title":"CrewAI","href":"/tools/crew-ai/"} language: "en" canonicalUrl: "https://tools.utildesk.de/en/ratgeber/multimodale-agenten-warum-bild-video-und-code-jetzt-in-einem-workflow-landen-ein/" --- The era of purely text-based AI is drawing to a close. Over the past few years, we have become accustomed to language models writing excellent copy or correcting code fragments, but a fundamental paradigm shift is now taking place. We are witnessing the rise of multimodal agents that no longer just read and write, but can see, hear, and act independently in complex visual environments. This is no longer about separate tools for different media types, but about seamless integration, where image, video, and code merge into a single coherent workflow. ## Relevant tools on Utildesk If you want to do more than just categorize the topic and actually compare tools in practice, these products and frameworks are a good starting point: - [Claude](/tools/claude/) — if you want to test agentic coding sessions in the terminal or IDE against real-world use. - [GitHub Copilot](/tools/github-copilot/) — as a reference point for the productive copilot layer directly in the editor. - [Cursor](/tools/cursor/) — if you want to compare a more agentic IDE workflow with its own working context. - [Aider](/tools/aider/) — if you prefer to steer Git-native coding sessions directly from the terminal. - [LangChain](/tools/langchain/) — if you want to understand the orchestration logic and framework layer behind agents. - [CrewAI](/tools/crew-ai/) — if you are interested in collaborative multi-agent flows with guardrails and observability. ## The technical core: native multimodality instead of helper interfaces The key difference from earlier systems lies in the architecture of the underlying models. For a long time, visual perception functioned only as a pre-processing module that translated information for a language model. New approaches such as GLM-5V-Turbo break with this principle: here, multimodal perception is a native core component of logic, planning, and execution. The model "sees" the user interface or a video directly as part of its reasoning process, rather than relying on an external description. This native integration allows agents to interpret heterogeneous contexts such as websites, documents, GUIs, and videos directly. In practice, that means significantly higher reliability in tool use and in executing tasks that require visual feedback. If an agent is supposed to operate complex software, it must understand how the graphical interface changes after an interaction — a capability that GLM-5V-Turbo deliberately trains through integrated reinforcement learning and hierarchical optimization. ## Tools in action: from video commerce to automated demos The theoretical maturity of this technology is already reflected in a range of specialized applications. We are currently seeing a wave of tools that make multimodal capabilities usable for highly specific business processes. * **[Vyrill](/tools/vyrill/):** This platform uses agentic infrastructure to automate the search and monetization of video content. The goal is not simply playback, but a deep understanding of video content for e-commerce. * **[Naoma AI](/tools/naoma-ai/):** A specialized video AI agent for the B2B SaaS sector. It creates instant demos, massively accelerating the sales process by presenting product features accurately in both visual and contextual terms. * **[Hera](/tools/hera/):** This tool focuses on producing launch videos in studio quality. It combines AI-assisted video production with an agentic approach to create professional marketing materials more efficiently. For developers and power users, frameworks such as MiniMax CLI make it possible to give custom agents native multimodal capabilities. At the same time, heavyweights like Xiaomi are entering the market with the MiMo-V2-Pro and Omni models to establish flagship solutions for omnimodal applications. Specialized high-speed models such as GLM-5-Turbo, optimized for frameworks like [OpenClaw](/tools/openclaw/), also show that latency in processing visual data is dropping dramatically. ## Practical assessment: what this means for teams and workflows For teams integrating AI workflows into their processes, the focus is shifting from pure text generation to process automation in visual environments. A classic scenario would be software quality assurance: an agent could not only analyze the code of a web app, but actually open the app in the browser, identify visual defects, and at the same time propose the fix in the code repository. What matters most when selecting tools is the depth of integration. Teams should check whether a tool is merely a wrapper around a language model or whether it is based on native multimodal models that can process visual data without loss of information. The ability to plan hierarchically and verify end-to-end reliably, as highlighted in GLM-5V-Turbo, is an important quality marker for stable workflows. An agent that visually checks its own steps produces far fewer errors than a system that blindly executes chains of commands. ![AI-assisted video production with a multimodal workflow](/images/ratgeber/multimodale-agenten-warum-bild-video-und-code-jetzt-in-einem-workflow-landen-ein-workflow.webp) ## Limits, risks, and necessary guardrails Despite the impressive progress, using multimodal agents comes with specific trade-offs. Processing image and video data in real time requires massive compute resources, which can lead to higher costs and potential latency issues. The complexity of error sources also increases: an agent may misinterpret a user interface because of faulty UI rendering, leading to unpredictable actions. Another risk lies in verification. While text responses can be checked relatively easily with automation, validating a chain of visual interactions is much more demanding. Robust guardrails and monitoring systems are essential here. Developers must ensure that agents operate in closed environments, especially when they have access to sensitive GUIs or production data sources. Reliance on visual perception quality also means that small changes in a website’s design can affect agent performance unless it has been trained for strong generalization. ## Conclusion: the convergence is irreversible The development makes one thing clear: the separation between text AI, image AI, and video AI was artificial, and native multimodal architectures are now overcoming it. Models like GLM-5V-Turbo show that integrating perception and reasoning leads to more powerful, more autonomous agents. For companies, this represents a major opportunity: complex tasks that previously required human "eyeballing" are moving into the realm of automation. We are at the beginning of a development in which AI agents become real partners in visual and technical workflows. The ability to write code while evaluating the visual result in real time will become the new standard for productive AI systems. Anyone who sets the right course today and integrates multimodal strategies into their workflows will benefit from gains in efficiency far beyond what was possible with text-only agents. ## What you should do next To avoid falling behind this development, a gradual approach is recommended. Start by checking your current text-based workflows for visual bottlenecks. Wherever employees currently switch back and forth between screenshots, videos, and text descriptions, that is where multimodal agents have the greatest potential. Experiment with specific frameworks such as MiniMax CLI to get a feel for controlling multimodal functions. If your focus is marketing or sales, tools like [Naoma AI](/tools/naoma-ai/) or [Hera](/tools/hera/) offer a quick entry point without requiring your own model infrastructure. Keep a close eye on the development of native models such as GLM-5V-Turbo, as they will define the technological foundation for the next few years. Evaluation and verification should be part of your setup from the start to ensure the reliability of your automated processes. ## Sources 1. [GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents](https://arxiv.org/abs/2604.26752) 2. [Vyrill Agentic Video Commerce Platform](https://www.producthunt.com/products/vyrill) 3. [Naoma AI Demo Agent](https://www.producthunt.com/products/naoma) 4. [Everybody wants to rule the AI world](https://www.theverge.com/podcast/926707/openai-ceo-murati-musk-trial-vergecast) 5. [MiniMax CLI](https://www.producthunt.com/products/minimax) 6. [Hera Launch](https://www.producthunt.com/products/hera-6) 7. [GLM-5-Turbo](https://www.producthunt.com/products/z-ai) 8. [MiMo-V2-Pro & Omni](https://www.producthunt.com/products/mimo-3)