Claude Managed Agents + Cloudflare Run AI Agents at Scale with Full Control

Why This Integration Matters

AI agents are becoming the new building blocks of software. But running them reliably at scale—especially when they need to execute code, browse the web, or access private services—requires more than just a clever prompt. You need infrastructure that can handle millions of concurrent sessions without breaking the bank.

That's where Cloudflare's new integration with Claude Managed Agents comes in. It decouples the "brain" (the agent loop running on Anthropic's platform) from the "hands" (the execution environment). You get to choose where your code runs, how your agents connect to the world, and how you observe every action they take.

Let's break down what this means for developers and platform engineers.

Key Features: What You Get Out of the Box

The integration provides a ready-to-deploy template that includes:

• Enhanced security: All agent traffic runs through customizable proxies, allowing you to inject credentials securely, prevent data exfiltration, and observe interactions.
• Sandbox control and observability: Detailed metrics, logs, SSH access, and customizable images.
• Lightweight sandboxes: Choose between traditional microVMs or lightweight V8 isolates for millisecond boot times and massive scale.
• Private service connectivity: Connect to internal services via Cloudflare Mesh or Workers VPC without exposing them to the Internet.
• Browser control and observability: Audit trails, session recordings, and human-in-the-loop flows.
• Email capabilities: Each agent gets its own email address for sending and receiving messages.
• Custom tools: Extend agents with your own functions without managing extra infrastructure.

Code Example: Adding a Custom R2 Tool

Here's a snippet from the integration repo that shows how easy it is to add a custom tool—this one uploads files to R2 and returns a public URL:

// custom-tools.js – add a tool to host files on R2
defineTool({
  name: "r2_host_file",
  description: "Upload from sandbox to R2 and get a public URL.",
  inputSchema: z.object({
    key: z.string().describe("Object key"),
    content: z.string().describe("UTF-8 file body"),
    contentType: z.string().describe("MIME type"),
  }),
  run: async ({ key, content, contentType }, { env }) => {
    await env.PUBLIC_BUCKET.put(key, content, { httpMetadata: { contentType } });
    return `${env.PUB_R2_URL.replace(/\/$/, "")}/${encodeURI(key)}`;
  }
});

Just add the binding in your wrangler config and you're done.

Comparison: MicroVMs vs. Isolates for Agent Sandboxes

Limitations & Considerations

• Isolates are not full Linux environments: If your agent needs to run arbitrary Linux binaries (e.g., apt-get), you'll need a microVM.
• Security complexity: While proxies add a layer of defense, misconfiguring egress policies could expose sensitive data. Always test thoroughly.
• Observability overhead: Logging every action can generate massive data volumes—plan your retention and shipping strategy.

Next Steps to Learn More

• Dive deeper into Cloudflare's developer platform with our Log Explorer multi-vector attack forensics guide.
• Understand how to validate data integrity in distributed systems with the Netflix Data Canary pattern.

Conclusion: Your Agent Infrastructure, Your Rules

The collaboration between Cloudflare and Anthropic marks a shift toward more flexible AI agent deployment. By separating the reasoning loop from execution, you gain the freedom to optimize for cost, security, and performance without being locked into a single vendor.

Start with the official getting started guide (source) to deploy in minutes. Then, experiment with custom tools and egress policies to tailor the environment to your exact needs.

The future is agentic—make sure your infrastructure is ready.