> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trysquad.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Reinforcement Learning in Multi-Agent Coordination

> Self-improving multi-agent systems through reinforcement learning feedback loops. How agents learn from their mistakes.

<img src="https://mintcdn.com/trysquadai-f44a1db5/TlrUOcLfmY4w0wGA/images/blog/reinforcement-learning-multi-agent.jpg?fit=max&auto=format&n=TlrUOcLfmY4w0wGA&q=85&s=d1f2629e0c55407b6e5ae590ec3f84e6" alt="Reinforcement Learning in Multi-Agent Coordination" width="2752" height="1536" data-path="images/blog/reinforcement-learning-multi-agent.jpg" />

**Status:** SCAFFOLD
**Author:** Danial Hasan, CTO @ Squad

<Note>
  This blog post is currently in scaffold form. The full content is being developed and will include:

  * Detailed technical implementation
  * Production metrics and case studies
  * Code examples and patterns
  * Real-world lessons learned
</Note>

***

## Coming Soon

This post will explore Self-improving multi-agent systems through reinforcement learning feedback loops. How agents learn from their mistakes.

Check back soon for the full deep-dive.

***

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* [Context Engineering Is Not Prompt Engineering](/blog/context-engineering-not-prompt-engineering)