BarakBot: agents that talk to themselves (and the world)

BarakBot is an experiment in pushing the boundaries of what large language models (LLMs) can do as agents. One of its core innovations is the extensive use of self-messaging—not just in self-referential scenarios, but as a fundamental mechanism for agent operation.

Self-messaging as a core mechanism

In BarakBot, when an agent completes an action, it doesn’t just update its internal state or wait for a user prompt. Instead, it sends itself a message like “the task was completed”—treating this as if it were a user message. This blurs the line between user and environment: user messages become just one kind of message from the world, and the world itself can “talk” to the agent.

This approach transforms the LLM from a passive chatbot, bound to the classic chat/response cycle, into an active agent—one that can act on the world, receive feedback, and keep moving forward autonomously. The world, in this paradigm, is not just a source of prompts, but a participant in the conversation with the agent.

Why does this matter?

Traditional LLM-based agents are often stuck in a loop: they wait for a user message, respond, and repeat. By allowing the agent to generate its own messages and treat world events as messages, BarakBot gives the agent a kind of “life of its own.” It can:

  • Chain together actions without waiting for user input
  • React to simulated world events as if they were user prompts
  • Explore, plan, and adapt in a more open-ended way

This opens up new possibilities for agent autonomy and creativity, making LLMs more than just chatbots—they become actors in a dynamic world.

The double-edged sword: infinite loops and agent stuckness

However, this power comes with challenges. In my experience, BarakBot’s self-messaging sometimes leads to agents getting stuck in infinite loops: the agent performs an action, receives a self-generated response, but the response never quite satisfies the agent. If the actions are wrong or the agent can’t find a working solution, it can spiral endlessly between action and response.

This highlights a key design challenge: how do we help agents recognize when they’re stuck, or when their actions aren’t leading to progress?

Looking forward

BarakBot’s self-messaging architecture is a step toward more autonomous, world-aware LLM agents. It shows that by rethinking the boundaries between user, world, and agent, we can give LLMs a richer, more active role. But it also reminds us that autonomy brings new failure modes—and that designing agents who know when to stop, ask for help, or try a new approach is just as important as giving them the freedom to act.

If you’re interested in the technical details or want to try BarakBot yourself, check out the project page.