7-lesson course · about 14 hours

Run a supervised Factory of AI agent teams for your content series

For Creators, educators, influencers and personal-brand teams responsible for useful content, audience trust and a consistent production practice

Everything for Creators

Connect research, production and release agent teams on one shared record. Push a late correction into every copy, and approve each piece yourself before it goes out.

Try lesson 1 freeSee what you’ll learn

What you’ll be able to do

By the end, you can…

Give each team a job and an ownerSet up Research and Editorial, Production, and Release and Learning teams. Each one gets named agents, a deliverable, limits on what it may do and a named human owner. On a one-person channel every owner is you, and you write that down.
Keep one record of what's trueBuild a shared record that ties every claim to its source and date, and to every script, caption, on-screen text, chapter, pinned comment, group post and spoken line that uses it. Add acceptance checks for each handoff and a route that sends failed work back with a reason.
Point Dots or Claude Duet at the approved recordChoose OpenAI Dots or Claude Duet based on the apps you use and the limits you need. Then set it up to follow the record across handoffs and bring back source-linked drafts, a list of changed claims and the choices only you can make.
Carry a correction into every copyWhen the 5:30 last shuttle becomes 4:30, hold everything that depends on it and fix the text versions. Spoken lines go back to you to re-record or cut. Then open each re-export and check its captions, on-screen text and transcript against the record.
Find out where the Factory breaksTest it with a changed input, a blocked task and a restart days later. Set a retry limit and an escalation owner for each team, tighten the instructions, and run a second small batch to compare.
Stay the author at releaseApprove, return or hold each piece with a written reason, and record who may publish it. Review disclosure and sponsor wording, check the release against your own AI policy, and write a next-series checklist another operator could follow.

How it works

A tutor that meets you where you are

  1. Lessons that start at your levelEach of the seven lessons adapts to what you already know. If you've run single agents for intake and research packets, you won't be walked through basics again. If a step is new to you, the lesson slows down there.
  2. An AI tutor you can ask anythingAsk questions typed or out loud while you work. You can ask the tutor to check your team assignments, walk through tracing the 5:30 claim with you, or test your acceptance checks against your own series sample.
  3. Quizzes and flashcards for each lessonShort quizzes and flashcards are tied to each lesson, so you can check that you understand ideas like return routes, held items and escalation owners before you move on.
  4. Progress tracking and a certificateYour progress through the three modules is saved, so you can stop and pick up where you left off. You receive a certificate of completion when you finish the course.

Curriculum

The curriculum

Three modules follow one series from setup to sign-off. First you build the teams and the shared record. Then you run handoffs and push a factual correction through every copy. Last, you tighten the process, run a second batch and record your decision.

Module 1Give a content series a shared operating plan.2 lessons

  1. 1Assign the series proactive AI agent teams
    • Explain why three teams sharing one record are needed when each single agent can be right and a fact can still go out wrong
    • Assign named agents, a deliverable, authority limits and a human owner to each of the three teams
    • Write a series audience promise, evidence standard and decision record for your own series or the cliff-walk case
    • Set voice, likeness and privacy limits that apply to every team
  2. 2Define the acceptance checks
    • Build a shared record linking each claim to its source, date and every script, caption, on-screen text, chapter, pinned comment, group post and spoken line that uses it
    • Write acceptance checks for a usable packet, an edit-ready script and a reviewable video
    • Design a return route that sends failed work back to the right team with a stated reason
    • Test the record by tracing one claim, such as the 5:30 shuttle, to every place it appears

Module 2Keep the proactive AI agent teams aligned when a claim changes.2 lessons

  1. 3Run the series handoffs
    • Choose between OpenAI Dots and Claude Duet for your series based on the apps you use and the limits you need
    • Configure the agent to work from the approved record and return source-linked drafts, changed claims and open choices
    • Run a series packet through all three handoffs and apply the acceptance checks at each one
    • Log each pass, return and decision in the shared record
  2. 4Propagate a factual correction
    • Trace every copy of a changed claim through the shared record and hold each dependent item
    • Separate the fixes agents may make in text from the spoken lines only the creator can re-record or cut
    • Check each re-export's captions, on-screen text and transcript against the corrected record
    • Draft the corrected reply to the family for the creator to review and send

Module 3Approve the process for the next series.3 lessons

  1. 5Refine the proactive AI agent team instructions
    • Test how the teams respond to a changed input, a blocked task and a restart days later
    • Set a retry limit and a named escalation owner for each team's failed checks
    • Revise source rules and voice guidance in the standing instructions based on what failed
    • Explain why corrections go into standing instructions rather than one-off notes
  2. 6Run a second sample batch
    • Run a second batch through all three teams using the refined instructions
    • Check that each new claim enters the record with an approved source and date, or is held as an unknown
    • Compare the second run's returns, escalations and drift with the first run
    • Decide which instruction changes to keep, revise or drop
  3. 7Record the creator’s decision
    • Approve, return or hold each piece with a written reason, and record who may publish it
    • Review disclosure and sponsor wording against the platforms' and regulators' primary sources
    • Check the release against a personal AI policy and state what the operation is for
    • Write a next-series checklist another operator could follow

Who it’s for

Made for you if…

Made for: Creators, educators, influencers and personal-brand teams responsible for useful content, audience trust and a consistent production practice

A solo creator answering audience questions on several platforms

You already use a proactive agent for intake or research packets, but one fact now lives in a short, a carousel, a chapter title, a pinned comment and your own voice. This course shows you how to keep all of those copies tied to one record, with you as the owner of every team.

A creator working with a part-time editor or manager

You need clear lines on who owns which agent, what each one may do and who may publish what. You'll write those down, set acceptance checks for each handoff, and keep the facts, the voice and the release with the creator.

An educator or personal-brand team running a content series

Your audience acts on what you tell them. You'll practice the expensive mistake, a corrected fact that has already reached scripts and edits, and build a checklist so the next series runs the same way.

Questions

Good to know

What do I need before I start?

Days 1–7 of the Influencer MasterCourse or the same experience. That means writing honest, sourced scripts that keep verified facts apart from personal observations, and having used OpenAI Dots or Claude Duet for a standing job such as intake, research packets or script drafts. You should also be able to edit short video and export captions in a tool like CapCut, and have a claim-to-source packet and a voice guide for your channel, or be willing to build simple ones first.

Do I need my own series to work on?

It helps to bring three to five related audience questions from your own channel. If you don't have them, you can use the course's cliff-walk case, which runs from a family's question about the walk to the 5:30-to-4:30 shuttle correction, plus a second batch on the dogs and winter-route questions.

Which agent does the course use, Dots or Claude Duet?

Both are taught as equals. You choose one based on the apps you use and the limits you need, then set it up to follow the approved record and report changed claims and open choices.

Will the agents publish posts or send replies for me?

No. Agents draft and the creator approves claims, disclosures, publication and audience replies. Auto-publishing, and letting agents send, sign, pay or delete, are outside this course.

Does the course use voice cloning or a digital twin?

No. The course sets a firm rule that no agent alters or synthesizes the creator's voice or face. When a spoken line carries a fact that has changed, it goes back to you to re-record or cut.

Will this help me grow my audience?

This course is about a production process you can reuse. It doesn't cover growth, reach or viral strategy, and it doesn't explain performance from a single metric. It makes no promises about audience growth.

Does it cover FTC rules and platform AI labels in detail?

You'll review disclosure and sponsor wording against the platforms' and regulators' primary sources. The course doesn't give detailed legal advice on the FTC Endorsement Guides or on TikTok and Meta labeling rules, so check the primary sources before relying on specifics.

Do I need to write code?

No. Building custom software or code integrations between agents isn't part of the course. In CapCut or similar tools, manual export steps are marked rather than automated.

Influencer AI Capstone: a supervised Factory for your content series

$397

One payment · lifetime access

  • 7 self-paced lessons in three modules: setting up the teams and record, running handoffs and corrections, and approving the next series
  • Hands-on practice with OpenAI Dots or Claude Duet on your own series sample or the cliff-walk case
  • The 5:30-to-4:30 correction traced through captions, slides, chapters, pinned comments, group posts and spoken lines
  • Tests for changed inputs, blocked work and a later restart, then a second sample batch
  • An AI tutor you can ask typed or out loud, with quizzes and flashcards for each lesson
  • Progress tracking and a certificate of completion

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Refunds: Full refund if you ask before you open your first lesson. Once you’ve started, purchases aren’t refundable, except where the law gives you that right.

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