Every API is a Tool for Agents with Code Mode

on YouTube ↗36m

" summary

Claim: dumping OpenAPI into LLM context hits a 2.3M token wall; solution is agents generating and running TypeScript against a typed SDK inside sandboxed Dynamic Worker Loaders. Live demo builds a 20-endpoint project‑management API, shows one‑round‑trip workflows, and runs untrusted LLM‑generated code in V8 isolates with ~1ms cold starts. Results: massive token savings, composable control flow, and practical safety controls for network and resource access.

AI-written summary · about

" chapters (23)

1. 0:00Intro
2. 0:41Beyond the Glass Box: Giving AI Agents Hands
3. 1:16Your 20% Off 🤫
4. 1:53The Evolution of MCP: From Bundle Tools to Global Sharing
5. 3:30The Context Limit Nightmare: Cloudflare’s 2.3M Token Problem
6. 4:41The Core Thesis: Context Limits are an Agent Problem
7. 5:05Progressive Disclosure 101: CLI Bash & Self-Documentation
8. 5:30The Brittle Reality of Semantic Tool Search
9. 6:02Code Mode Unveiled: Discovering Tools on Demand
10. 8:20Hidden Inefficiencies: Why Search Costs 17,000 Tokens
11. 9:52Typed SDKs: Generating Reliable Pipelines for LLMs
12. 11:55The Safety Dilemma: Running Untrusted AI-Generated Code
13. 13:04Control Flow Mastery: Solving Brittle AI Workflows
14. 14:52Massive Token Efficiency: One Round Trip to Rule Them All
15. 16:28Architecture Suicide? The History of Sandboxing
16. 18:29Dynamic Worker Loaders: The Ultimate Agent Sandbox
17. 20:30Network Lockdown: Controlling Fetch in Isolate Environments
18. 21:40Live Demo: Managing Enterprise Projects via Code Mode
19. 24:27MCP Evolution: Bringing Server-Side Complexity to AI
20. 26:27Scaling to Cloudflare: Managing 2,600 Global Endpoints
21. 27:44Action Demo: Deploying Infrastructure via AI Chat
22. 31:38Final Thoughts: The Future of Agents & Hiring
23. 32:21⁉️ Q&A: Handling Massive Specs & API Discovery Patterns

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