I ran an interesting test for AI agents: to create a presentation on the history of Earth’s geological eras, complete with text and images.
The task involved action planning, information gathering, content organization, layout design, and file format conversion. This allowed for an assessment of current AI agent capabilities, their practical usability, and potential bottlenecks.
I tested four AI agent products: Skywork, Coze Space, Manus, and Lovart. Here’s how they performed: 👇
Skywork
Skywork had the highest completion rate, being the only tool that successfully outputted a PPT file.
See the full result here: https://tiangong.cn/share/v2/ppt/1925788478895357952?dataType=outfile&outputId=1925788478895357952&outputType=gen_ppt&projectId=1925782838113832960&sharingId=1925797872445526016
Upon receiving the task, Skywork initiated a scope confirmation process. I provided as much detail as possible, and its final output was the most comprehensive among the agents tested.
Next, it planned by creating a task list, which it referred to throughout the execution.
The execution was lengthy, primarily involving searching and browsing. Here’s an excerpt:
After gathering sufficient information, it first drafted a PPT outline.
The final PPT generation involved creating about a dozen web pages, which were then displayed together.
The conversion to PPT slides and merging into a single file only occurred during download, making the process lengthy. Downloading the HTML format resulted in a folder containing these separate web pages.
However, the resulting PPT file wasn’t very practical. Due to inconsistent page dimensions during generation, each slide varied slightly in size, often leaving blank space at the bottom.
Additionally, minor layout errors from the web page generation phase meant the final result wasn’t perfect.
However, it required minimal manual adjustment, indicating considerable potential.
Coze Space
Coze Space couldn’t directly generate PPTs, providing a document instead. However, since the format wasn’t critical, this was still considered a task completion.
See the full result here: https://space.coze.cn/s/bSmamok4LFg/
Its execution process was simpler but followed a similar pattern: planning, data gathering, sourcing web images, and content integration.
I specifically enabled two extensions for Coze Space—Feishu Docs and an image generation tool—to see if it would utilize them. It used neither. The report wasn’t written to Feishu Docs, nor were images generated and inserted. This was expected, as I hadn’t explicitly instructed it to use them. Besides, for this kind of report, web images are preferable to generated ones; aesthetics weren’t the priority.
Manus
Manus provided a text-only PDF, essentially failing the task.
See the full result here: https://manus.im/share/DdcDQMgzQ59pWvI2akPuiD?replay=1
Its execution process was logical, however.
Although there wasn’t a distinct planning step, the final file included a to-do list, suggesting an underlying plan.
It searched for images during execution but saved very few, with none saved successfully.
This resulted in a plain text report.
Lovart
This agent focuses on design, serving a different purpose. I included it for comparison to see its output.
See the full result here: https://www.lovart.ai/r/62cce51
Design-focused agents operate differently; Lovart treated this task as creating an infographic.
It began by seeking visual inspiration while gathering information on geological eras.
Its execution plan was roughly: organize information, generate four images for four geological eons, and then design the layout.
It produced a long, webpage-style image and marked the task as complete.
Thoughts
The subject of this test, geological history, involves readily accessible information that doesn’t demand complex reasoning. I briefly reviewed the details and found the information from each agent largely accurate, so I didn’t perform an in-depth check. My primary aim was to evaluate their effectiveness in science communication and their capacity to translate specialized knowledge into formats easily digestible by the public.
Different AI agent products possess distinct ‘DNA’ and employ varied approaches. Whether they prioritize content or presentation, neither approach is inherently superior or inferior. This helps identify their respective strengths; when used judiciously, they can effectively address specific problems.
Notably, Skywork and Lovart surpassed basic document generation, employing technical methods to enhance content presentation. This capability isn’t exclusive to AI agent tools. AI design expert 歸藏 (Guīcáng) demonstrated similar AI design capabilities using prompts long ago. In other words, the core of an agent’s design ability still lies in the prompt.
For those less skilled in prompt engineering, AI agent tools offer a viable alternative, significantly lowering the entry barrier. However, for more customized content presentation, carefully crafted prompts in general AI tools can achieve this, though it necessitates a separate information-gathering step.
Finally, to answer the initial question: Can AI independently create PPTs now?
If this means creating a usable PPT file with reliable and substantial content, then the answer is no.
However, if you can ensure content quality yourself, and AI’s role is merely to convert that content into a more digestible visual format (not necessarily PPT files), then the answer is yes.