How I Built an AI Agent That Makes Videos While I Sleep

Discover AnythingSignupWrite New StoryHow I Built an AI Agent That Makes Videos While I SleepbyMrclbyMrcl|@mrclhnzCEO at https://www.safebird.aiManagement consultant by day, builder by night. Shipping daily. chief human at ➡️ http://safebird.aiSubscribeJuly 22nd, 2026TLDR Your browser does not support the audio element.Speed1xVoiceDr. One Ms. Hacker byMrcl@mrclhnzbyMrcl|@mrclhnzCEO at https://www.safebird.aiManagement consultant by day, builder by night. Shipping daily. chief human at ➡️ http://safebird.aiSubscribeStory's CredibilitybyMrcl|@mrclhnzCEO at https://www.safebird.aiManagement consultant by day, builder by night. Shipping daily. chief human at ➡️ http://safebird.aiSubscribeStory's Credibility Video example is at the end.Somewhere in a Railway datacenter in the Netherlands, a small worker process wakes up on a cron schedule. Over the next 2 minutes and 20 seconds it invents five video ideas, argues with itself about which one is best, writes a script, checks the script against my brand rules, generates a German voiceover and a music bed, renders a 30-second vertical video, and uploads it to YouTube. Then it goes back to sleep for three days. Total cost for the last days: $1.91. Five videos. My involvement: zero minutes. The videos are for Safebird, the agentic operating system we’re building for insurance brokers. In my last article I argued that the founders winning right now build systems, not just products. This is what that actually looks like, a running process with a heartbeat. Here's exactly how it's built, including the version that failed, the actual prompts, and the guardrail that kills a run before it can spend my money on an off-brand video. What the machine actually does The agent is a pipeline of seven steps, and they come in three distinct tiers of predictability. Only three steps use an LLM, GPT-5.6 Terra Pro via OpenRouter, and each of those is a separate, small agent with its own persona, its own prompt, and its own temperature. Two more steps are generative but bounded: ElevenLabs synthesizes the voiceover and the music in single one-shot calls, with no feedback loop. And the remaining steps, the guardrail lint, the Remotion render, the upload, are plain deterministic code: same input, same output, every time. ImageNotice the shape of it. Three creative steps, then a hard gate, then the production steps. The creative part is cheap (about $0.23 of LLM calls). Everything after the gate costs real money: voice synthesis, music generation, render compute. So the gate sits exactly where the money starts. That ordering is not an accident. It's the whole design philosophy in one sentence: Judgment before spend. The version that failed (and why) I didn't start here. Version one did what everyone does first: I let the LLM create the visuals. The LLM authored raw Remotion TSX code for every scene, including layouts, animations, positioning, all of it. Then, because LLM-generated layouts are unreliable, I added a second LLM pass to look at rendered frames and grade them, a multimodal image-QA loop that regenerated scenes until they passed. It technically worked. It was also a disaster: ~€6 per video: per-scene code generation plus the image-QA loop burned tokens like a furnace. Contrast issues, text overflow, visual artifacts: a model that writes brilliant German copy turns out to be a mediocre layout engine. Hard API failures: GPT-5.6 Terra Pro is a reasoning model, and reasoning tokens count against your max_tokens budget. One-shot generations of ~500 lines of TSX would hit 500 errors. The monolithic "generate everything in one call" approach didn't just look bad, it died at the API level. The fix came from accepting a simple truth about what these models are actually good at: LLMs are excellent at picking and filling. They're bad at pixel-perfect creation. So I took the pixels away from the model. I hand-designed a library of ten parameterized scene templates: kinetic headlines, quote cards, chat