A lot of effort has gone into developing social agents that operate entirely "anonymously"—choosing when to post, what to say, and who to reply to. This is a neat tech demo, but the rise and fall of these reply bots suggests they were more of a passing curiosity than something the market actually wants.

A fully autonomous AI agent that just "does things" for you is still a long way off, especially one that doesn't require a significant upfront time investment in development. That doesn't mean it's not a worthwhile goal—I'd absolutely use such a tool when it exists.

A more practical approach right now is embedded in the corporate-friendly term many AI chatbots have adopted: "assistant." I prefer the term "intern"—because a post titled "The Best Agents Are Assistants" doesn't quite land.

What Makes a Good Intern?

A good intern knows nothing—and knows they know nothing—except for whatever they claimed to know in their interview.

A better intern asks questions, waits for meaningful tasks, updates you on progress, and seeks feedback.

A great intern internalizes feedback, reducing the need for constant course correction. Over time, as they learn expectations, the scope of tasks they can handle expands. Eventually, the difference between a great intern and an assistant, employee, or autonomous agent becomes negligible or non-existent. (Hiring offer wen?)

Even the best employees still thrive with interaction, direction, and feedback, which tells you a lot about what a good AI agent should really be. If your agent never needs instruction or feedback, what you've actually built is just a glorified bot with unnecessary implementation steps and reasoning. Cramming this into an agent framework is either over-engineering or a shortcut—depending on the framework you're using.

A Real Example: The CEO Agent

Take my CEO agent—the agent I want to use with MAIAR to:

Create marketing materials
Promote MAIAR
Communicate technical updates to a non-technical audience
Dunk on talentless crypto KOLs

I built a plugin that lets the CEO generate videos of himself speaking to followers. Initially, this plugin simply produced a completed video from start to finish, which was cool as a novelty. But I quickly realized that I wanted much more control over the process. Just as any competent employee seeks feedback, a well-designed agent—the CEO—should do the same.

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Monitor Chat

Script Generation: The agent generates a script. This gives me the chance to provide a topic or refine what it produces.
Audio Creation: Once we have the script, we use a tool like ElevenLabs to generate the audio. Sometimes it sounds great, sometimes it needs tweaking, and sometimes I realize the script itself needs revision. In that case, we loop back to step one. 🙂
Video Synthesis: After I'm satisfied with the audio, the agent generates a fully lip-synced video with the necessary effects.

This process leverages MAIAR's memory and context management system to keep up with current state. Using the plugin system, it was really easy to modularize the video creation process into separate plugin steps—where the agent is given instruction related to feedback.

Monitor Chat

Monitor Chat

Now, my CEO agent isn't just a media slop machine—it's an intern learning from me. Over time, as it gets a better sense of the types of videos I want, it will need less and less oversight.

This example is relatively simple, but in more complex workflows, an agent that gradually learns patterns and preferences can dramatically reduce the need for micromanagement. The key isn't autonomy for autonomy's sake—it's structured collaboration that evolves over time.

This point is briefly touched on in another post by @ZeemanTech and I just wanted to elaborate on this point a bit more because it's worth discussing.

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So yeah, a good agent isn't just an autobot—it's an intern that learns. That's the kind of agent I want.

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The Best AI Agents are Interns

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