AIAgents

147 trillion tokens. 80 million virtual computers. A few months of operation. Those numbers don’t just describe a successful startup—they describe infrastructure that’s already running at scale while most AI agent companies are still writing whitepapers. Meta just paid roughly $2 billion for that head start, and if you think this is just another acqui-hire, you’re missing what’s actually happening. What Manus Actually Built Before we get into why Meta cares, let’s talk about what Manus is. Imagine you need to research a complex topic—say, comparing healthcare policies across twelve countries, pulling recent legislative changes, and summarizing how they’d affect a specific demographic. You could spend three days doing that yourself, or you could hand it to an AI agent that breaks down the task, spins up the necessary tools, and delivers a structured report while you grab coffee. ...

Key Highlights Agent compatibility is crucial for efficient tool-space interaction in the MCP era Designing for compatibility at scale is essential for avoiding interference and ensuring seamless interaction The MCP era requires a new approach to tool-space design, focusing on agent-centric development The MCP era has brought about a significant shift in the way we approach tool-space interaction. With the increasing use of artificial intelligence and machine learning, agents are becoming an integral part of our systems. However, this shift also introduces new challenges, particularly when it comes to ensuring compatibility between agents and tools. As we move forward in this era, it’s essential to understand the importance of designing for agent compatibility at scale. ...