Use case
NotionGoogle DriveX (Twitter)AGNT

Content production, from idea to published

Run research, drafting, review and publishing as one workflow: agents do the legwork, humans hold the gates that matter.

Content production, in brief

The jobRun research, drafting, review and publishing as one workflow: agents do the legwork, humans hold the gates that matter.
Tools it needsNotion, Google Drive, X (Twitter), YouTube, Unsplash
Setup4 steps, about ten minutes
Runs whenOn a schedule you choose
ApprovalYours, per category — nothing is sent on your behalf unless you say so
Where it runsYour own machine. Credentials stay in a local vault.

What the agent actually does

A first draft that is already sourced

Before anything is drafted, an agent gathers and reconciles sources, so the piece is built on material somebody can check rather than on the model’s recollection of the internet.

Draft in your voice

Style lives in a skill the agent loads — your vocabulary, your structural habits, the things you never say. That is a reviewable file rather than a prompt somebody tweaks until it sounds acceptable.

Publish everywhere at once

One approved piece becomes the post, the thread, the newsletter section and the summary, each shaped for its destination rather than the same text pasted four times.

The tools this job needs

Notion

Where the work arrives. The team wiki agents can actually keep current. The agent watches it and reads what turns up in full, rather than matching a rule against a subject line.

Google Drive

Context the agent pulls in before deciding. Where the documents live — and pile up. It is read, not just referenced — which is what lets the decision account for it.

X (Twitter)

Context the agent pulls in before deciding. The feed where your market thinks out loud. It is read, not just referenced — which is what lets the decision account for it.

YouTube

Where the result lands. Long-form content worth repurposing everywhere else. Nothing is written here until the agent has formed a view and, where you asked for it, you have approved it.

Get this agent

Content Producer

Run research, drafting, review and publishing as one workflow: agents do the legwork, humans hold the gates that matter.

Runs on your own machine. Asks for Notion, Google Drive, X (Twitter) and nothing else — you approve the connection, and the agent inherits whichever AI model you already use.

You can see exactly what it did

Nothing happens behind your back.

Every run leaves a receipt: what the agent read, which tools it called, what it decided and why, and precisely what it changed. Anything irreversible — sending, paying, publishing, deleting — waits for you to approve it. It runs on your own machine, with your own credentials, and the whole trail is yours to read afterwards. The point is not that you trust it. The point is that you never have to.

Pipelines beat prompts

Asking a model for a finished article produces something plausible and unusable, because writing is not one task. Research, outline, draft, edit against a standard, adapt per channel — each is a separate step with its own inputs and its own failure modes. Modelling them as separate steps means you can put a human gate exactly where taste is required and automate the rest honestly.

Voice is the part that has to be captured, not prompted

The reason machine-written content reads like machine-written content is that nobody encoded what makes their writing theirs. Doing it properly means writing down the specifics: sentence rhythm, the constructions you avoid, how much hedging is acceptable, worked examples of good and bad. Kept as a skill, that becomes an asset the whole team shares rather than a knack one person has.

The gates that actually matter

Two are non-negotiable: a human approves the outline before drafting, and a human approves the piece before publishing. The first prevents a beautifully written article about the wrong thing; the second is the last line between your brand and an embarrassing sentence. Everything in between can run unattended.

Build it in AGNT

  1. Capture your voice and standards as a skill the drafting agent loads.
  2. Set the research step to gather sources and produce an outline for approval.
  3. Draft, self-edit against the standard, and route to a human for the publish decision.
  4. Fan the approved piece out to each channel in that channel’s native shape.

The definition, ready to paste

Here is the shape in full rather than in prose: a trigger, a decision, an action. Paste the definition below into a new workflow and it loads exactly as drawn.

Content production, from idea to published: watch notion → read it and decide → act in google drive TRIGGER notion-api Watch Notion Fires the moment it happens. ACTION agnt-agent Read it and decide Judged against your own rules. ACTION google-drive-api Act in Google Drive Runs LIST_FILES.
Nothing leaves your machine: AGNT talks to Notion and Google Drive directly, and each run leaves a receipt.
Content production, from idea to publishedpaste into a new workflow
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      "text": "Act in Google Drive",
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      "category": "action",
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        "action": "LIST_FILES"
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Get this running today

Free, local, and it asks for Notion, Google Drive, X (Twitter), YouTube, Unsplash and nothing else.

Common questions

Will this produce content that reads as AI-written?

It will if you skip the voice work. A captured style guide with real examples is the difference, and it is the step most teams skip and then blame the model for.

Can it publish without me?

Technically yes; advisedly no. The recommended shape keeps a human approval before anything goes public.

Does it work for technical content?

Yes, and the citation step matters more there. Technical claims should be traceable to documentation or source, which is exactly what the research step produces.

Give AI a job. Get the proof.