Cursor, v0, chai.new, lovable, bolt — what do they all have in common? They weren’t built on AI frameworks—they're built using primitives optimized for speed, scale, and flexibility.

LLMs are evolving fast—like, literally every week. New standards pop up (looking at you, MCP), and APIs change faster than you can keep track. Frameworks just can't move at this speed.

In this talk, I'll challenge conventional engineering wisdom, sharing my real-world experience scaling thousands of AI agents to handle over 100 million monthly runs.

You'll discover how using AI primitives can dramatically speed up iteration, provide bigger scale, and simplify maintenance.

I'll share eight practical agent architectures—covering memory management, auto tool integration, and simple serverless deployment—to help you quickly build reliable and scalable AI agents.

By the end of this session, you'll clearly see why we must rethink and rebuild our infrastructure and focus on AI-native primitives instead of heavy, bloated, and quickly outdated frameworks.

I wonder if we need another S3-moment but for the AI agent infrastructure.