# Automate LinkedIn posts with n8n: AI agents that research, write, and publish on approval Build an n8n pipeline that researches a topic, drafts a LinkedIn post in two competing styles, generates an image brief, and publishes to LinkedIn only after you approve it in Airtable. Published: 2026-06-05 Updated: 2026-06-05 Reading time: 8 min Canonical: https://www.fusionsync.ai/workflow/posts/linkedin-post-automation-ai-agents-n8n Markdown: https://www.fusionsync.ai/workflow/posts/linkedin-post-automation-ai-agents-n8n/markdown Tags: n8n, LinkedIn, AI agents, Airtable, Gemini, content Posting consistently on LinkedIn is a content-supply problem, not a willpower problem. The blank page wins because researching a topic, writing it in a voice that does not sound like a press release, and shipping it on a schedule is real work. Most "AI post generators" solve the wrong half: they spray generic text with zero research and zero human gate, and your feed starts to read like everyone else's. This guide builds the version that actually holds up. You approve a topic, AI agents research it with live search, two different copywriters draft competing versions, an image brief gets generated, and the draft lands in Airtable for review. Nothing publishes to LinkedIn until a human flips a status to **Approved**. Speed with a gate. ## Available resources This build uses two assets you will set up below: 1. **n8n workflow** "LinkedIn - Post Automation" (research + drafting + publishing). 2. **Airtable base** "Linkedin Post Automation" with two tables: **Topics** and **LinkedIn Posts**. [Download workflow JSON: LinkedIn - Post Automation](/workflows/linkedin-post-automation-ai-agents-n8n.json) ## What you'll need Before you begin, make sure you have: 1. An **n8n account** (cloud or self-hosted) with the workflow editor. 2. An **Airtable account** to manage the Topics and LinkedIn Posts tables. 3. A **Google Gemini API key** for the research and writing agents (any LLM works; this build uses Gemini). 4. A **SerpApi key** so the research agent can search the live web. 5. **LinkedIn API access** (a connected LinkedIn page or profile credential in n8n) to publish. ## Overview of the automation The automation runs in two phases that share one Airtable base. A human approval sits between them. 1. **Draft creation.** Approving a topic triggers a research agent, then one of two copywriting agents (chosen at random), then an image-brief agent. The finished draft is written to the LinkedIn Posts table as `Ready For Review`. 2. **Publishing.** Approving a draft triggers the LinkedIn node to publish the post, then flips its status to `Posted`. LinkedIn: published post (only after approval) Airtable: full audit trail of topics, drafts, and statuses The important design decision is the **approval gate between phases**. Research and drafting are cheap and reversible. Publishing to a company page is neither. Keeping them in separate trigger-driven flows means a bad draft dies quietly in Airtable instead of going live. ## Step-by-step setup ### 1. Set up the Airtable base Create a base (the example names it "Linkedin Post Automation") with two tables. **Topics** drives phase one: ```text Topics (single line text) the headline idea Discription (long text) context for the research agent Status (single select) Approved, Researched, Decline ``` **LinkedIn Posts** holds the generated drafts and drives phase two: ```text LinkedIn Post (long text) the generated post body Image Description (long text) the structured image brief Status (single select) Ready For Review, Approved, Posted, Decline ``` > **Match field names to the workflow exactly** > > The Airtable nodes reference fields by their literal names, including the misspelled `Discription`. If you rename a field, update every node that maps to it, or the expression returns empty. ### 2. Connect credentials in n8n Add four credentials so the agents and publishing node can authenticate: 1. **Airtable** (personal access token) with read and write on the base. 2. **Google Gemini** for the chat models behind every agent. 3. **SerpApi** for the research agent's `Search Tool`. 4. **LinkedIn** for the page or profile you publish from. ### 3. Build phase one: research and draft **Trigger on an approved topic.** An `Airtable Trigger` watches the Topics table. A `Filter` node (`Status = Approved`) lets only approved rows through, so you stay in control of what gets researched. **Research the topic.** The `Topic Research Agent` runs on Gemini with two tools wired in: a `Search Tool` (SerpApi) for live information and a `Think` tool for reasoning. A `Structured Output Parser` (wrapped in an `Auto-fixing Output Parser`) forces clean JSON: ```json { "Idea": "", "Description": "", "Significance": "", "Implementation": "" } ``` The auto-fixing wrapper matters: LLMs occasionally return slightly malformed JSON, and it re-prompts the model to repair the output instead of failing the run. **Pick a writing style at random.** A `Code` node returns a random 1 or 2, and a `Switch` routes to one of two copywriting agents: ```js function getRandomNumber() { const randomNum = Math.floor(Math.random() * 2) + 1; return [{ json: { randomNumber: randomNum } }]; } return getRandomNumber(); ``` - **Point of View agent**: a conviction-led, conversational take that opens with a strong statement. - **PAS agent**: the Problem, Agitate, Solution framework for higher-engagement posts. Randomizing the style stops your feed from falling into one repetitive template. Both agents receive the same research input: ```text Idea: {{ $('Topic Research Agent').item.json.output.Idea }} Description: {{ $('Topic Research Agent').item.json.output.Description }} Significance: {{ $('Topic Research Agent').item.json.output.Significance }} Implications: {{ $('Topic Research Agent').item.json.output.Implementation }} ``` **Merge and generate an image brief.** A `Merge` node collapses the two possible branches back to one (only the chosen branch carries data). The `Image Description` agent then converts the finished post into a structured visual brief (main scene, key elements, style, colors, what to avoid) ready for any image generator. **Write the draft to Airtable.** A `Create Record` in LinkedIn Posts stores the post body and image brief with `Status = Ready For Review`, and an `Update record` marks the source topic `Researched` so it is not picked up again. ### 4. Build phase two: review and publish **Trigger on an approved draft.** A second `Airtable Trigger` watches the LinkedIn Posts table, and a `Filter` (`Status = Approved`) only proceeds once a human has reviewed the draft and flipped the status. **Publish to LinkedIn.** The `LinkedIn` node posts the approved text: ```text {{ $json.fields['LinkedIn Post'] }} ``` **Close the loop.** An `Update record` sets the row to `Posted`, so your Airtable becomes a clean history of what shipped and what was declined. ## Testing the workflow Validate each phase before trusting it with your real page: 1. **Approve a topic.** Add a row to Topics with an idea and description, set `Status = Approved`, and wait. Within a minute or two a new row should appear in LinkedIn Posts. 2. **Read the draft.** Confirm the `LinkedIn Post` field has a researched, on-voice post and the `Image Description` field has a structured brief. Run it a few times to see both the POV and PAS styles appear. 3. **Approve the draft.** Set the LinkedIn Posts row to `Status = Approved`. 4. **Confirm it published.** Check that the post is live on LinkedIn and the row flipped to `Posted`. Decline a draft instead and confirm nothing publishes. If a draft never appears, check the research agent's output parser first: a malformed JSON response is the most common failure point, which is exactly why the auto-fixing parser is wired in. ## Customization options - **Swap the model.** Replace Gemini with OpenAI, Anthropic, or any chat model n8n supports. The agents and parsers stay the same. - **Add more writing styles.** Extend the `Switch` to three or four branches (storytelling, listicle, contrarian) and widen the random range in the `Code` node. - **Auto-generate the image.** Feed the `Image Description` brief into an image model and attach the result to the LinkedIn post instead of stopping at the brief. - **Route approvals through Slack.** Post the draft to a Slack channel with Approve and Decline buttons instead of editing Airtable by hand. - **Schedule publishing.** Add a delay or a scheduled queue so approved posts go out at peak engagement times rather than immediately. ## Common mistakes that quietly break this - **No human gate before publishing.** Wiring the LinkedIn node straight off the writing agent means one hallucinated claim goes live on your company page. Keep the approval filter. - **Skipping the auto-fixing parser.** A raw structured parser fails the whole run on the first malformed JSON. The auto-fixing wrapper repairs and retries. - **Renaming Airtable fields without updating nodes.** Expressions reference field names literally (including `Discription`); a rename silently returns empty values. - **Letting the agent write without search.** Without the SerpApi `Search Tool`, the research agent invents specifics. Live search is what keeps posts current and credible. FusionSync AI builds workflows like this one end-to-end. [Hire FusionSync AI](https://fusionsync.ai/contact) ## Conclusion You now have a LinkedIn content engine that does the heavy lifting (research, drafting in two voices, and an image brief) while keeping a human firmly in charge of what actually publishes. Approve a topic and a reviewable draft appears in Airtable; approve the draft and it goes live, with every step logged. Feed it a steady list of topics and the blank page stops being the reason you went quiet. Need help setting this up? [Book a call](https://cal.com/fusionsyncai/n8n-hub-call-booking).