If you’re still writing posts one at a time, your content pipeline is already obsolete. On the latest Zero to Agent in 30 Minutes, Craig Hewitt, founder of Castos, demonstrated how to turn a fresh Hermes installation into a social media agent that can study a person’s writing, draft posts, and plan recurring research, focusing on the context, workflows, and safeguards that help an agent produce useful work. Once set up, the always-on agent can run on a schedule, monitor external sources, and complete recurring tasks without human oversight. Check it out.
How to build a social media agent that researches and writes LinkedIn posts
- Choose the right agent setup. Decide whether you need an interactive tool for active work or an always-on agent that runs on a schedule. Craig used the Hermes desktop app for the demonstration, which gives him the option to deploy it to a cloud server or dedicated computer later.
- Create a structured workspace. Ask the agent to organize a new project with separate files for voice guidance, editorial standards, post templates, examples, and operating instructions. A clear file structure gives the agent reliable information to retrieve as it works.
- Seed the agent with relevant context from your own work. Provide examples of your own posts, emails, and other writing that reflect the style you want. Craig also included examples of writing he likes from people he follows to give the agent a broader range to analyze.
- Turn the examples into a voice system. Have the agent analyze the material and document its findings. The voice profile captures the audience, point of view, sentence style, recurring themes, editorial rules, and types of posts to create.
- Test a narrow workflow with human review. Start with one task, such as drafting several LinkedIn posts from a supplied idea. Keep a person in the loop while you evaluate the output, correct mistakes, and refine the instructions.
- Package repeatable work into skills. Create reusable instructions for recurring tasks such as researching topics, selecting a post format, retrieving relevant examples, and drafting in the approved voice. Craig compared these skills to standard operating procedures that make recurring tasks more consistent.
- Connect the agent to fresh data. Add sources of new ideas, such as news feeds, websites, social platforms, or internal business systems. Craig recommended starting with a simple, semiautomated trend scan before investing in a more complex data pipeline.
- Add triggers and safeguards. Decide what starts each workflow, whether that’s a schedule, a user request, a webhook, or a change in another system. Use separate accounts and limited permissions for autonomous agents so you can trace their actions and control their access.
Agents become useful when they have context, clear processes, the right tools, and enough oversight to validate each workflow. Once those pieces are in place, Craig noted, teams can gradually move from one-off prompting to systems that monitor information and complete recurring work.
Coming next week
In the next episode, Max Johnson, cofounder of briix.ai, will take a workflow that only lives in someone’s head at the moment (or maybe is captured in a messy Notion doc or a long email chain) and rebuild it as an autonomous agent, live and from scratch. You can follow along with every decision as you learn how to spot the steps that can be handed off, how to handle the ones that can’t, and how to structure the whole thing so it runs without you.
Ready to take your agent knowledge further? Learn to design and build production-ready agentic infrastructure by attending Harness Engineering for AI Agents on August 12. And if you want to go deeper with Hermes, join us for Build Your First Local Agent with Hermes on August 26.
