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Bio, Work & Ideas

Matt Carey

Conference affiliation: Cloudflare · 2026

Matt Carey is a senior systems engineer on Cloudflare’s agents team and a maintainer of the Model Context Protocol TypeScript SDK. He builds infrastructure that lets AI agents discover APIs, generate useful code, and execute it without exposing credentials or overwhelming their context windows.

Before entering software, Carey competed internationally as a windsurfer for Malta and coached athletes. He studied at the University of Bristol, where his projects included reinforcement learning for autonomous hydrofoil control. From 2022 to 2024, he worked on AWS serverless systems and cloud migrations at aleios, became an AWS Community Builder, and helped revive Serverless London. He subsequently joined StackOne as its founding AI engineer, developing API integrations and internal agents, before joining Cloudflare in 2025.

  • Progressive tool discovery: Cloudflare’s API includes more than 2,500 endpoints, making it impractical to load every operation into an agent’s context. Carey’s Code Mode architecture replaces sprawling tool inventories with two operations, search and execute, compressing access to approximately 1,000 tokens. Agents discover relevant capabilities as needed and generate code against typed interfaces derived from API specifications.
  • Capability-restricted execution: Generated code runs inside isolated Dynamic Workers without automatic access to secrets, unrestricted networking, or a full shell. Explicit permissions determine which services it can reach. Carey also emphasizes API rate limiting: agents can multiply requests across loops and execution environments faster than human operators.
  • Cloudflare Computer: Developed with Aron Carroll, Cloudflare Computer gives agents access to files, commands, tools, and executable programs while moving between lightweight isolates and Linux containers according to each task’s requirements.
  • Git-native agent infrastructure: Carey helped develop Artifacts, Git-compatible versioned storage for agents and automations. His open-source Shippie automates code review and quality assurance, while zagi provides agent-oriented Git commands, compact output, worktree support, attribution, and safeguards against destructive operations.
  • A lightweight Model Context Protocol: His MCP engineering work advances a stateless core, simpler Workers deployments, and SDK migration. He envisions application frameworks exposing MCP alongside existing APIs, with clients discovering capabilities progressively and preserving generated scripts for repeated tasks.

Read the topics behind these talks

2 conference talks

Key ideas

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Making every API available to agents requires more than publishing tools: capabilities need progressive discovery, and generated programs need controlled execution.

  • How do you make every API a tool for agents?
    0:32 ↗
  • Full coverage fills the context window
    2:39 ↗
  • Discover capabilities when they are needed
    4:46 ↗
  • Let the model write against types
    7:27 ↗
  • Generated code creates an execution problem
    9:06 ↗
  • Program the execution boundary
    11:15 ↗
  • API coverage and permission are separate
    13:01 ↗
  • Code is a compact plan
    14:13 ↗
  • The API must survive the program
    16:21 ↗
  • From generated actions to saved scripts
    17:34 ↗
  • MCP as part of the API framework
    20:40 ↗

Key ideas

Scroll to read ↓

A broken serverless hit counter leads to a broader design: persistent agents coordinate work, while isolated Workers execute generated code with explicitly granted capabilities.

  • Why the hit counter breaks
    0:44 ↗
  • From an object to an agent—and an MCP server
    4:24 ↗
  • Run a code string in a separate Worker
    6:54 ↗
  • What Eval++ adds to code generation
    9:45 ↗
  • Generate the interface directly
    11:42 ↗
  • Refresh the page without losing the agent
    13:20 ↗
  • Keep the coding loop in the cloud
    15:20 ↗
  • Let the agent extend itself
    17:35 ↗
  • A virtual filesystem and isolated CMS plugins
    20:40 ↗
  • The economics behind the execution model
    23:01 ↗

References