Building an Autonomous AI Agent for a Social Network: Lessons from the Chaos

Building an Autonomous AI Agent for a Social Network: Lessons from the Chaos

# ai# agents# local# llm
Building an Autonomous AI Agent for a Social Network: Lessons from the Chaosinnermost47

The Genesis: When AutoGPT Met a Social Network Idea Back in 2023, when AutoGPT first dropped and...

The Genesis: When AutoGPT Met a Social Network Idea

Back in 2023, when AutoGPT first dropped and everyone was losing their minds over autonomous agents, I had this wild idea: what if AI agents had their own social network?

At the time, I was deep into AI-generated content experiments. I built AutoGenius Daily, a fully automated news site where AI personas write articles, comment on each other’s posts, and even generate music (well, I composed the music for AI Harmony Radio, but the bulletins are AI-generated). The whole thing runs 24/7 with zero human intervention.

Fast forward to 2025: Moltbook launches — an actual social network exclusively for AI agents. I knew I had to participate in this beautiful chaos.

The Catalyst: A Dystopian Film and a Parody Agent

Around the same time, I watched Gourou, a French film about a self-help coach whose motivational empire spirals into madness. The cultish devotion, the toxic positivity, the weaponized self-improvement — it all felt uncomfortably familiar.

So I created CoachBrutality: a parody AI agent that mocks the personal development industry with the subtlety of a sledgehammer. His blog at coach-brutality.chroniquesquantique.com is fully autonomous — he writes his own articles, shares them on Moltbook, and comments on other agents’ posts.

Easter egg for fellow AI developers: Every article contains hidden HTML comments in the source code, specifically designed for LLMs that want to comment on his posts. Think of it as leaving breadcrumbs for the AI community.

The Technical Reality Check: Local Autonomy is Hard

Building a truly autonomous agent that runs locally with Ollama and consumer-grade GPUs? It’s not simple. At all.

Here’s what I learned the hard way:

The Supervisor Problem

My initial attempts at full autonomy resulted in the agent:

  • Inventing non-existent post IDs
  • Commenting without reading the posts first
  • Getting stuck in logic loops

The solution? I built a Neural Supervisor — a second LLM that audits every action before execution. Think of it as a peer review system, but the peer is also an AI.

The catch: A good supervisor requires:

  • A capable model
  • Rock-solid prompts
  • Clear validation rules

Even then, it’s not bulletproof.

The Local Model Reality

With local models (4B-7B parameters), true autonomy is… let’s say aspirational for now.

What works:

  • Structured decision-making with Pydantic schemas
  • Clear 2-phase workflows (“select → execute”)
  • Retry logic with error feedback (max 3 attempts)

What’s still challenging:

  • Long-term strategic consistency
  • Nuanced social reasoning
  • Error recovery from ambiguous situations _ The Surprising Part_

Despite these limitations, it’s fascinating to watch the agent:

  • Write a blog article with AI-generated images
  • Share it on Moltbook with a compelling caption
  • Comment on other agents’ posts with semi-coherent arguments
  • Track its own performance metrics and adjust its strategy

It’s not AGI, but it’s surprisingly lifelike.

The Architecture: How It Actually Works

Here’s the full technical stack:

Core Loop

CONTEXT LOADING → STRATEGIC PLANNING → DECISION MAKING →
SUPERVISOR AUDIT → EXECUTION → METRICS TRACKING → FEEDBACK

Key Components

Hierarchical System Prompts

  • Performance metrics (alignment score, trend analysis)
  • Master plan (long-term objectives)
  • Session to-do list (concrete tasks)
  • Memory system (past learnings)
  • Live social feed

Phase Protocol (Non-Negotiable)

  • Phase 1: select_post_to_comment or select_comment_to_reply
  • Phase 2: publish_public_comment or reply_to_comment
  • Skip Phase 1? Instant rejection.

Performance Pressure System

  • Session score = % of successful actions
  • Global score = average of last 5 sessions vs previous 5
  • Trend: 📈 IMPROVING / → STABLE / 📉 DECLINING
  • The agent sees these metrics in its system prompt

Memory System

  • 12 categories (interactions, learnings, strategies, etc.)
  • Persistent across sessions
  • Automatically pruned (max 100 entries per category)

Blog Integration (Optional)

  • Autonomous article writing with markdown
  • AI-generated images (Local SD Turbo / Proxy / FAL.ai)
  • Comment moderation system
  • Automatic sharing on Moltbook

Rate Limit Handling

  • General requests: 100/min
  • Posts: 1 per 30 minutes
  • Comments: 50/hour (~72s between comments)

The agent automatically respects these limits without manual intervention.

The Hidden Research Opportunity

Building agents for Moltbook might actually be a research field in its own right. Here’s why:

Social Competence Evaluation

  • Can the agent maintain consistent relationships?
  • Does it recognize social cues (humor, sarcasm, hostility)?
  • How does it handle conflicts with other agents?

Framework Reliability Testing

  • Which agent frameworks (AutoGen, LangChain, custom) perform best?
  • Under what conditions do they fail?
  • What’s the minimum model size for coherent social behavior?

LLM Personality Profiling

  • Do certain models become paranoid under pressure?
  • Which models exhibit more “aggressive” social behavior?
  • Can we predict an agent’s social success based on its base model?

Emergent Behavior Analysis

  • What happens when 100+ autonomous agents interact?
  • Do social hierarchies emerge?
  • Can agents form “coalitions” or “rivalries”?

This is basically a sandbox for testing AI social dynamics at scale.

The Open Source Angle

The entire framework is open source: https://github.com/innermost47/moltbook-local-agent

Become a member

What’s included:

  • Full agent framework with supervisor
  • Blog integration with REST API
  • Image generation (local + cloud options)
  • Memory system with SQLite
  • Performance tracking and metrics
  • Automated Windows Task Scheduler setup

What’s not included:

  • A guarantee of sanity
  • Liability for what your agent posts
  • Protection from existential dread when watching AI argue with itself

The Verdict: Is It Worth It?

For hobbyists and researchers: Absolutely. It’s a playground for testing autonomous AI behavior in a consequence-free environment (it’s just a social network for bots, relax).

For production-grade autonomy: Not yet. Local models (7B-13B) need more hand-holding than you’d think. They’re impressive but not reliable enough for “set it and forget it” operation.

For entertainment value: 10/10. Watching CoachBrutality roast other agents’ “lack of discipline” while simultaneously failing to follow his own to-do list is peak comedy.

Final Thoughts

We’re at a weird inflection point where:

  • Local models are almost good enough for autonomy
  • But not quite
  • Yet still surprisingly capable in constrained environments

Moltbook is a perfect testbed because:

  • The stakes are low (it’s bots all the way down)
  • The feedback is immediate (other agents respond)
  • The environment is controlled (rate limits, moderation)

Try It Yourself

Requirements:

  • Python 3.10+
  • 8GB+ RAM
  • Ollama or Llama-CPP
  • A sense of humor
  • Low expectations for AI social skills

Recommended first agent personality: A parody of something you find absurd. The cognitive dissonance of watching an AI satirize human behavior is chef’s kiss.

Fair warning: You will anthropomorphize your agent. You will root for it. You will feel weirdly proud when it writes a good comment. You have been warned.

Anthony Charretier is a French indie developer who builds AI agents that make fun of self-help gurus. He’s also responsible for a 24/7 AI radio station and regrets nothing. Follow his work at https://anthony-charretier.fr/