In the field of image generation, we have long operated in an "input – output" mode. You write a prompt, the model generates an image, and if the result isn't perfect, you have to adjust the prompt and try again. However, this process is inefficient for professional creative work. As stated in the new update of the AgentSwarms platform, the biggest challenge isn't the generation itself, but the so-called routing nightmare – that is, the logistical nightmare of managing communication between different models so that they effectively collaborate within the creative process.
The Problem of Single-Shot Prompting
Today's standard text-to-image models are fascinating, but their interaction with humans is linear. The user must be the "director" who must know exactly what they want, constantly trying to guess which word in the prompt will change the background color or the number of fingers on a character's hand. For companies that need a consistent visual identity, this process is too demanding in terms of time and human resources.
AgentSwarms solves this problem using multi-agent systems. Instead of one model, it uses an entire "team" of specialized AI agents. One agent can function as a creative director (creates the concept), another as a prompt engineer (translates the concept into the model's language), and another as a visual critic (analyzes the resulting image and looks for errors). This process repeats until the desired standard is achieved.
Technical Background: How Tokens and Pixels Connect
To understand why connecting these systems is so challenging, we need to look at how the individual parts work. According to expert analyses, such as the study From Tokens to Pixels, these models operate on completely different principles:
- LLM (Large Language Models): They work on the principle of predicting the next token. Tokens are small pieces of text that the model assembles into logical wholes. LLMs are experts on semantics and instructions.
- Image Generators (Diffusion models): They function as experts in noise removal (denoising). They start with random noise and step by step create a structured image from it according to instructions.
Connecting these two worlds requires precise "routing". The agent must take visual information from the image (using vision models), convert it back into text tokens that are understandable to the LLM, and subsequently use these tokens to modify the original input. AgentSwarms automates this process through its new Image generation playground.
Comparison: AgentSwarms vs. Traditional Tools
If we compare this approach with commonly available tools, the differences are clear:
| Feature | Midjourney / DALL-E 3 | AgentSwarms (Agentic Workflow) |
|---|---|---|
| Working Method | Single-shot (one prompt) | Iterative (cycle of criticism and correction) |
| Quality Control | Dependent on human | Autonomous (AI critic) |
| Workflow Complexity | Low | High (requires orchestration) |
| Regular users, artists | Developers, creative agencies |
Practical Impact for Czech Companies and Creators
What does this mean for the Czech market? For small marketing agencies in Prague or Brno that don't have budgets for teams of graphic designers, this tool can be a way to speed up the production of visual content for social networks. Instead of hours spent tuning prompts, the agency team can define parameters and let a "swarm" of agents develop several variants, which they then only finally approve.
However, it is necessary to take into account the EU AI Act regulation. Within the European Union, generated media content must be transparent. AgentSwarms and similar systems that enable complex iterations will in the future have to meet strict requirements for labeling (watermarking) and explain how the image was created in order to prevent disinformation. For Czech companies, this means that when implementing these tools, they must ensure compliance with rules regarding the transparency of AI-generated content.
Price and Availability
AgentSwarms is primarily a tool aimed at developers and professionals. Currently, there is no public price list available for the end consumer in Czech crowns, but the platform typically operates on a SaaS (Software as a Service) model with monthly subscriptions for developers (estimated from 20–50 USD per month for basic API and orchestration access). The tool is available globally via a web interface, which means it can be used in the Czech Republic as well, but for the best results in agent orchestration, English terminology is still recommended.
Do I need to know English to use AgentSwarms?
For operating the interface itself and writing instructions for agents, English is still preferred because most underlying models (LLMs) function most precisely in English. However, thanks to the capabilities of modern LLMs, you can enter instructions in Czech as well, but the resulting quality of the agent's "criticism" may be higher when using the English language.
Is this tool suitable for regular users who just want nice pictures?
Rather not. AgentSwarms is designed for process automation and workflow creation. If you are looking for a simple tool for quick image creation, Midjourney or DALL-E 3 are still the better choice. AgentSwarms is for those who want to build a system that creates images for them.
How does AgentSwarms deal with errors in details, such as hands or text?
That is precisely where its strength lies. Thanks to the integration of vision models (Vision LLMs), the agent can "see" errors in the image. If the critic discovers that a character has the wrong number of fingers, it sends instructions back to generation with a refined description, thereby trying to eliminate the error in the next iteration step.