AI Social Media Management: Agents and Prompts

For social media, use AI to draft, repurpose and sort, and keep a person approving every post and every reply. Feed it your own material, such as articles and release notes, so it works from facts. Connect it to a scheduler through a workflow tool, not straight to your accounts. Model cost per post is a fraction of a cent. Checked 31 October 2026.

The job in one table

What the job is

Social media management is a stream of small public acts: posting, replying, noticing. It rewards consistency and punishes mistakes in public. Most of the cost is in the routine: writing a variant for each platform, finding a time, reading mentions. AI can take a lot of the routine. It cannot know what happened in your business this morning, and it will happily write a cheerful post on a bad day.

The safest pattern is to give the model real material and ask for drafts, not ideas from nothing. A model asked to "write ten posts about our product" invents claims. A model asked to "turn this release note into three posts, using only the facts in it" does not.

Which agent fits

We have not listed a social platform's own server on this site, so we do not claim any add-on posts directly to a network. Check a scheduler's documentation for what it exposes.

Add-ons that help

Browse the full list at MCP servers, and see what MCP servers are.

Prompts and kit items

The release notes skill is a good input for social drafts: it produces a clean list of what changed, which a drafting step then turns into posts. The docs writer skill does the same for longer help content. The prompt library has social prompts by task, and the CLAUDE.md starter is the place to record your voice rules.

A voice sheet beats a vague "be friendly." Include three posts you like, five words you never use, how you treat emoji and hashtags, what you will not joke about and how you sign replies. Then the model has something concrete to match.

A safe workflow, step by step

1. Write the voice sheet and the list of banned topics.
2. Choose the source of truth: an article, a release note or a product update, in a folder or table.
3. Draft from the source only. Instruction: "use only facts in this text; if a claim is missing, ask."
4. Format per platform. Lengths and conventions differ; give the model each platform's current limits, taken from that platform's help pages.
5. Send drafts to an approval step, such as a channel or a table with a status column.
6. A person approves, edits or rejects, then schedules.
7. Log what went out, with the source and the approver.
8. Review replies by hand, with the agent sorting them into "thanks," "question," "complaint" and "needs a person."

Never connect an agent to a live posting account with permission to publish unattended in the first month.

What it costs

Model. From Anthropic's pricing page, checked 31 October 2026: Sonnet 5.5 is $2 per million input tokens and $10 per million output tokens; Haiku 5.5 is $0.10 and $0.50.

Worked example (the token counts are our assumptions). A draft with 1,500 input tokens (the source, the voice sheet) and 300 output tokens:

Automation. A workflow tool is the larger line. On our n8n pricing page, Starter is 20 euros a month on annual billing for 2,500 executions, Pro is 50 euros for 10,000, and the Community edition is free to self-host. n8n's pricing page counts workflow executions with unlimited steps, so one run of a flow is one execution however many steps it has. Zapier MCP uses 2 tasks per successful tool call against your plan. Check the Zapier and Make pricing pages for current plan prices.

Scheduler and people. The scheduler is a separate subscription. The real cost is the approver's time, so plan for ten minutes of review for a week of posts.

Risks and how to review them

Several AI model licences also forbid generating fake reviews or false online engagement. For example, the usage policy attached to Meta's Muse Spark Open lists fake reviews and other false online engagement among prohibited uses. Check the licence of any model you use. See open source LLMs.

Measure it

Compare over at least a month. Social metrics are noisy, so do not change three things at once.

From one article to a week of posts (example)

Say you publish a 1,500-word article each week. A repurposing flow might look like this, and each step is small enough to review.

The first step is the one that protects you. By extracting claims with their source sentences, you give the later steps nothing to invent. If a draft contains a number that is not in the extract, the reviewer knows to reject it.

Handling replies and mentions

Replies are riskier than posts because they are one-to-one and often emotional. A sensible split:

• Sort automatically. Thanks, question, complaint, spam, press, other.
• Draft for the easy classes. A thank-you or a link to an article can be drafted from your help pages.
• Escalate the rest. Complaints, anything mentioning safety, legal threats, journalists and repeat contacts go to a person with a summary.
• Send by hand. A person posts the reply, so tone and timing are theirs.

The same escalation list works in AI customer support, and the two jobs often share a queue. If customers reach you on social with problems, route those to your support process instead of resolving them in public threads.

Platform terms and labelling

Each network has its own rules on automation, on third-party tools and on how AI-generated media must be labelled, and these rules change. Before you connect any tool to an account, read the platform's current developer and automation terms, and prefer the network's official scheduling integration to a workaround that logs in as you. For paid or gifted promotions, advertising rules require clear disclosure, and the person approving the post is responsible for it. Keep a note of the date you last checked, because we cannot promise they have not changed since.

A simple weekly rhythm

A rhythm like this keeps AI in the preparation seat. After two months, look at how many drafts you published unchanged. If it is nearly all of them, tighten the review or you are not reading. If it is almost none, improve the voice sheet or stop using the drafts.

Common mistakes

• Asking for ideas from nothing. Always supply source material.
• Posting the same text everywhere. Platforms have different norms and lengths.
• Letting the model pick the time. Use your own audience data.
• Skipping the link check. A broken link in a scheduled post is a public error.
• Treating engagement as the goal. Replies and visits matter more than likes.

Who should do what

Related tasks: AI SEO tools for search traffic, AI customer support for people who reach you through social, and AI tools for business for choosing across jobs.

Our take

Social media is where AI saves the most routine time and where mistakes are most public. Draft from your own material, keep a person on the approve button and never let an agent reply to a complaint on its own. We built this page from vendor pricing and published guidance, have not run a social account with these tools and have marked platform rules and scheduler features for you to confirm at the source.