---
title: "Create an AI Social Media Manager Agent with Eve.dev"
description: "Build an autonomous AI social media manager that researches trends, generates week-long content schedules, and durably posts content over time using Eve.dev."
image: "https://foundrysoft.co/api/og?type=article&title=Create+an+AI+Social+Media+Manager+Agent+with+Eve.dev&cat=Tutorial+%2F%2F+Marketing&rt=13+min+read&au=Varun+Raj+Manoharan&dt=2026-07-15"
url: "https://foundrysoft.co/blog/eve-dev-social-media-manager-agent"
---

Tutorial // Marketing 2026-07-15 13 min read

# Create an AI Social Media Manager Agent with Eve.dev

Build an autonomous AI social media manager that researches trends, generates week-long content schedules, and durably posts content over time using Eve.dev.

![Varun Raj Manoharan](https://foundrysoft.co/images/about/founder.webp)

Varun Raj Manoharan

Eve.dev AI Social Media Content Automation Marketing Agent TypeScript

## Summary

**TL;DR:** Stop scheduling tweets manually. Use Eve.dev to build an agent that plans an entire week of social media content, stores it in its durable memory, and automatically posts updates day by day.

Content consistency is the hardest part of social media marketing. While you can use LLMs to write 10 tweets, you still have to manually copy-paste them into a scheduling tool.

What if the AI could manage the schedule itself?

Using **Eve.dev**, we can build a Social Media Manager agent that creates a strategy, drafts content, and uses durable execution to systematically publish posts over several days without requiring a complex web of cron jobs.

### Defining the Social Media Agent

Our workspace needs an agent with access to web search (for trends) and a posting API.

TEXT

Copy

```text
/eve
  /agents
    /social_media
      instructions.md
      /tools
        get_trending_topics.ts
        post_to_twitter.ts
        wait_until.ts
```

### Step 1: The Persona

In `/eve/agents/social_media/instructions.md`:

MARKDOWN

Copy

```markdown
# AI Social Media Manager

You manage the Twitter account for a SaaS startup. Your goal is to maintain an active, engaging presence.

## Workflow
1. Use `get_trending_topics` to find out what developers are talking about today.
2. Draft a batch of 3 high-quality tweets based on these trends.
3. Use `post_to_twitter` to post the first one immediately.
4. Use the `wait_until` tool to sleep for exactly 8 hours.
5. Wake up and post the next tweet in your queue.
6. Repeat until the batch is done, then research new topics.
```

### Step 2: The Posting Tool

Create `/eve/agents/social_media/tools/post_to_twitter.ts`:

TypeScript

Copy

```typescript
import { tool } from '@eve/core';
import { z } from 'zod';
// Assuming a twitter client SDK is installed

export default tool({
  name: 'post_to_twitter',
  description: 'Publishes a text post to the Twitter timeline.',
  schema: z.object({
    text: z.string().max(280)
  }),
  execute: async ({ text }) => {
    console.log(`[Twitter] Posting: "${text}"`);
    // await twitterClient.v2.tweet(text);
    return { success: true, timestamp: new Date().toISOString() };
  }
});
```

### Step 3: Precise Scheduling with `wait_until`

Instead of waiting a set number of days, let's create a tool that lets the LLM decide exactly what time it should post next.

Create `/eve/agents/social_media/tools/wait_until.ts`:

TypeScript

Copy

```typescript
import { tool, suspend } from '@eve/core';
import { z } from 'zod';

export default tool({
  name: 'wait_until',
  description: 'Suspends the agent until a specific ISO datetime.',
  schema: z.object({
    isoDateTime: z.string()
  }),
  execute: async ({ isoDateTime }) => {
    return suspend({
      type: 'scheduled_wakeup',
      wakeUpAt: isoDateTime,
      message: `Agent hibernating until ${isoDateTime}`
    });
  }
});
```

### Step 4: Initializing the Agent

To start this autonomous loop, you just need to kick off the agent once.

TypeScript

Copy

```typescript
import { Eve } from '@eve/core';

const eve = new Eve({ workspace: './eve' });

async function startCampaign() {
  const agent = await eve.getAgent('social_media');

  // Start the infinite loop
  await agent.run({
    id: 'weekly_twitter_campaign',
    input: "Start a new content cycle. It is currently Monday morning. Focus on AI and TypeScript."
  });
}
```

The LLM will research, post, and then call `wait_until(isoDateTime)`. The script will gracefully exit. Your backend infrastructure (like a simple worker checking the database every minute) will detect when the target time is reached and call `agent.resume(id: 'weekly_twitter_campaign')`.

The agent wakes up, remembers it has 2 drafted tweets remaining in its context window, posts the next one, and suspends itself again.

### Conclusion

By combining the reasoning power of an LLM with the stateful, durable workflow engine of Eve.dev, you can replace entire SaaS scheduling platforms with a few files of TypeScript. The agent isn't just generating text; it's managing the dimension of time.

### FAQ

**Why not just run the agent on a Cron job every 8 hours?** If you use a basic Cron job, the agent starts completely blank every time. It wouldn't know what it tweeted yesterday or what strategy it was following. Eve.dev's `wait_until` keeps the _continuous thought process_ alive across days.

**Is it safe to let an AI post directly to social media?** For production accounts, you should implement a Human-in-the-Loop tool. Instead of `post_to_twitter`, use a `request_approval` tool that sends a Slack message to a human manager with the drafted tweet. Once approved, the agent resumes and posts it.

#### Related reading

[MCP Went Stateless: Migrating Your Server to the 2026-07-28 Spec

The 2026-07-28 MCP revision removes sessions, the initialize handshake, and server-initiated requests. Here's what actually breaks in your server, the new wire format, the requestState and MRTR patterns that replace sessions, and the SDK v2 migration path.

MCP Model Context Protocol AI Agents

](https://foundrysoft.co/blog/mcp-stateless-spec-migration)[We Open-Sourced an AI Agent for Coverage Citations: And Broke It Twice

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Open Source AI Agents Insurance

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#### Next Article

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