PAX Protocol: How Our 13 AI Agents Talk Without Losing Context
📰 Dev.to AI
Learn how PAX Protocol enables 13 AI agents to communicate efficiently without losing context, solving the problem of expensive English prose updates
Action Steps
- Build a multi-agent system with 13 AI agents using a framework like Python and TensorFlow
- Implement the PAX Protocol to enable efficient communication between agents
- Configure the protocol to handle 200-token status updates
- Test the system to ensure agents can communicate without losing context
- Apply the PAX Protocol to other multi-agent systems to improve scalability
Who Needs to Know This
This is relevant for AI engineers and researchers working on multi-agent systems, as it provides a solution for efficient communication between agents
Key Insight
💡 PAX Protocol solves the problem of expensive English prose updates in multi-agent systems
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🤖💬 PAX Protocol enables 13 AI agents to communicate efficiently without losing context! #AI #MultiAgentSystems
Key Takeaways
Learn how PAX Protocol enables 13 AI agents to communicate efficiently without losing context, solving the problem of expensive English prose updates
Full Article
PAX Protocol: How Our 13 AI Agents Talk Without Losing Context When you run 13 AI agents simultaneously, they need to communicate. English prose is expensive — 200-token status updates add up fast when you have 13 agents firing every 30 seconds. We built PAX Protocol to fix this. Why Standard Agent Communication Fails at Scale Most multi-agent tutorials show agents passing natural language: <pre class="highlight p
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