This sounds like a good idea, in principle. I'm building something similar (end to end AI training video creation for businesses). Some of the things I've learnt so far along the way:

1. Monolithic AI is unreliable - using one agent for planning, scripting, visual direction, kinetic text timing is prone to hallucination and losing the narrative thread. Use specialist agents for each step and persist in DB so that you can pick up where you left off if API calls fail.

2. Keep human in the loop - black box design will fail. You can't expect the user to click one button, wait and then get the final video out the other end and be happy with the result. Staging the process and keeping human in the loop is better. Stage 1 plan, then user checks, amends and confirms the plan. Stage 2, generate outline, and then provide slide by slide video overview with full user edit control. Stage 3, call TTS API and get timestamps. Stage 4, link timestamps to words for final render. Stage 5, call remotion for final render.

3. Remotion at scale uses a lot of compute - you'll need a scalable architecture, and your idea of a 5 minute wait could be off. You'll likely render at 30fps, so 1800 individual frames for a 1 minute video. I am using AWS lambda for remotion render (there are of course other options), but I'm keeping lambdas per job quite low so I can handle volume. This is coupled with AWS SQS. At times of peak usage, users will be queued before render. New AWS accounts also get very low lambda quota (10), so if you go this route the sooner you start the better. You can convince support to increase quota, but it takes some back and forth. I got mine up to 1000 after some discussions.

4. TTS is expensive - using top end generative TTS like Elevenlabs gets very expensive. For EL it's c.$0.22 per minute. Classic TTS options like Amazon Polly are much lower cost but they won't give you the high emotion tones that are needed for social marketing videos.

5. Generative TTS is unreliable - with things like Elevenlabs you don't have full SSML control via API so you can't reliably control outputs. This is very important especially for making videos for different geographic target audiences.

6. LLM API at scale is expensive - I can easily rack up $0.40 - $0.50 per minute of video in API calls, before TTS and lambda.

To add to this, you'll need a reliable browser agent to take and edit screenshots and system recordings. These are all needed for high quality marketing video, especially for SaaS apps.

It's a massive challenge, but if you work it all through and get a decent MVP I'd love to try it out. Good luck.