How Do I Turn My Loom Walkthroughs and Screen Recordings Into AI Agent Context So My Agents Can Run the Process Without Me?

To turn a Loom walkthrough into AI agent context, you transcribe it, cut it into discrete steps with named inputs and outputs, write down the decision rules you were saying out loud, and load that file somewhere your agent can retrieve it. The video is raw material. The context file is the product.

Almost everyone stops at the video, then wonders why the agent still asks them what to do next.

Why Isn't a Loom Walkthrough Enough on Its Own?

Video is linear. Agents need instructions they can index and pull one at a time.

A Loom is 14 minutes of me narrating my own screen. There is no clean way for an agent to jump to minute nine, where I explain what to do when a client pays a deposit twice. So it summarizes the first 90 seconds, invents the middle, and confidently does step four before step two.

I learned that the annoying way. I had a couple hundred hours of recordings and assumed the agents would just watch them and figure it out. They did not.

The real gap is that a recording captures what I did, not why I did it. The why is the exact thing an agent needs when the situation changes, and the situation always changes.

So the work is a translation job. You are converting a performance into a spec. That spec is what people mean when they say context engineering for founders. It is not prompt writing. It is transcription, structure, and the rules you never wrote down because they live in your head.

How Do You Turn a Screen Recording Into AI Agent Context?

Here is the process I use. It takes me about 25 minutes per recorded process now, and the first one took me two hours.

1. Record in Loom, but narrate the decisions, not the clicks. Not "I'm clicking here." Instead: "I'm checking this row because last month this client paid twice, so I look for a duplicate invoice number ending in the same last four digits." The clicks are obvious from the video. The reasoning is not.

1. Transcribe it. I use Descript when I want to edit the transcript like a doc, and the OpenAI Whisper API when I want it scripted inside a pipeline. Both handle a 15 minute recording in under a minute.

1. Cut the filler. My raw transcripts run about 160 words per minute of video. After I strip the ums and the tangents, I usually land around 40 words per minute. That is a 75 percent reduction, and it is the single highest-value step.

1. Break it into steps. Every step gets five fields: trigger, inputs, tools, output, and failure mode. The failure mode field is the one everybody skips and the one that saves you. "If the invoice number is missing, stop and ask me. Do not guess."

1. Attach the artifacts. The actual email you sent, the actual spreadsheet, a screenshot of the screen you were on. An agent with the template behaves nothing like an agent without it.

1. Store it as markdown and load it. I keep plain markdown files in a folder, then either paste them into a Claude Project or a custom GPT, or push them into Pinecone or Supabase if I want retrieval across hundreds of files. Markdown because every model reads it and it diffs cleanly in git.

That folder, plus the routing that decides which file loads for which job, is the beginning of an AI agent operating system. It is also how you teach AI your business without re-explaining yourself every single chat.

What Does the Full Screen Recording to AI Agent Workflow Look Like End to End?

I did this with member onboarding at Masterminds HQ.

The source was a 16 minute Loom where I walked through adding a new member, setting up their access, sending the welcome email, and booking their intro call. Raw transcript came out around 2,600 words. After cleanup it was 700. After step breakdown it was nine steps on two pages.

Because of that, the whole thing became an n8n workflow with one Claude step in the middle. The agent reads the member's intake form, drafts the welcome email from the template I recorded, creates the calendar event, and posts a summary in Slack. I review the email before it sends. That review gate is still there on purpose, and I plan to remove it after about 50 clean runs.

The path was: Loom, transcript, markdown context file, n8n node, review step. Total build time was an afternoon. Before that, onboarding took me about 40 minutes per member and I did it myself every time.

How Do You Know Your Agents Can Actually Run the Process Without You?

You test it with fresh eyes, which means no chat history and no extra hints.

I open a new conversation, paste only the context file, and ask the agent to produce step one for a real member. Then step two. If it stops, that is data. Every question the agent asks me is a missing line in the context file. I answer it, write the answer into the file, and run it again.

Once it clears the fresh-eyes test, I run it on live work with a human check at the end. I count the runs until I stop correcting anything. That number is different for every process. Onboarding took about 20 clean runs. Our venue inquiry replies took four, because the context file was simpler.

The whole point is that the agent finishes the job, not that it looks busy. If you are still re-explaining your business every time you open a chat, you do not have context. You have a transcript and a hope.

FAQ

Do I have to re-record everything, or can I use old Looms? Use the old ones. Transcription and cleanup work the same on a two-year-old recording. What you cannot reuse is a Loom where you were silent and just clicked, because there is no reasoning to extract.

How long should each walkthrough be? Keep them under 20 minutes and cover one process per recording. If you go over, split it. I would rather have six 12-minute Looms than one 70-minute one, because the cleanup and step breakdown stay manageable.

Can I do this without a developer? Yes. Markdown files, Descript or Whisper, and a Claude Project will get you a working agent for most back office processes. The n8n or Zapier piece is where it starts to help to have someone technical, but you can hand them the context file and they can wire it up in an afternoon.

I wrote the full playbook for this. How to Build Your Own AI Agent Operating System walks you through the exact architecture I use, step by step: the context file format, the folder structure, the routing, and the review gates. You can get it at https://a.mastermindshq.business/ai-os-book.