Introducing Agent Lee - a new interface to the Cloudflare stack

While there have been small improvements along the way, the interface of technical products has not really changed since the dawn of the Internet. It still remains: clicking five pages deep, cross-referencing logs across tabs, and hunting for hidden toggles.

AI gives us the opportunity to rethink all that. Instead of complexity spread over a sprawling graphical user interface: what if you could describe in plain language what you wanted to achieve? 

This is the future — and we’re launching it today. We didn’t want to just put an agent in a dashboard. We wanted to create an entirely new way to interact with our entire platform. Any task, any surface, a single prompt.

Introducing Agent Lee.

Agent Lee is an in-dashboard AI assistant that understands your Cloudflare account. 

It can help you with troubleshooting, which, today, is a manual grind. If your Worker starts returning 503s at 02:00 UTC, finding the root cause: be it an R2 bucket, a misconfigured route, or a hidden rate limit, you’re opening half a dozen tabs and hoping you recognize the pattern. Most developers don't have a teammate who knows the entire platform standing over their shoulder at 2 a.m. Agent Lee does. 

But it won’t just troubleshoot for you at 2 a.m. Agent Lee will also fix the problem for you on the spot.

Agent Lee has been running in an active beta during which it has served over 18,000 daily users, executing nearly a quarter of a million tool calls per day. While we are confident in its current capabilities and success in production, this is a system we are continuously developing. As it remains in beta, you may encounter unexpected limitations or edge cases as we refine its performance. We encourage you to use the feedback form below to help us make it better every day.

What Agent Lee can do

Agent Lee is built directly into the dashboard and understands the resources in your account. It knows your Workers, your zones, your DNS configuration, your error rates. The knowledge that today lives across six tabs and two browser windows will now live in one place, and you can talk to it.

With natural language, you can use it to:

• Answer questions about your account: "Show me the top 5 error messages on my Worker."
• Debug an issue: "I can't access my site with the www prefix."
• Apply a change: "Enable Access for my domain."
• Deploy a resource: "Create a new R2 bucket for my photos and connect it to my Worker."

Instead of switching between products, you describe what you want to do, and Agent Lee helps you get there with instructions and visualizations. It retrieves context, uses the right tools, and creates dynamic visualizations based on the types of questions you ask. Ask what your error rate looks like over the last 24 hours, and it renders a chart inline, pulling from your actual traffic, not sending you to a separate Analytics page.

Agent Lee isn't answering FAQ questions — it's doing real work, against real accounts, at scale. Today, Agent Lee serves ~18,000 daily users, executing ~250k tool calls per day across DNS, Workers, SSL/TLS, R2, Registrar, Cache, Cloudflare Tunnel, API Shield, and more. 

How we built it

Codemode

Rather than presenting MCP tool definitions directly to the model, Agent Lee uses Codemode to convert the tools into a TypeScript API and asks the model to write code that calls it instead.

This works better for a couple of reasons. LLMs have seen a huge amount of real-world TypeScript but very few tool call examples, so they're more accurate when working in code. For multi-step tasks, the model can also chain calls together in a single script and return only the final result, ultimately skipping the round-trips.

The generated code is sent to an upstream Cloudflare MCP server for sandboxed execution, but it goes through a Durable Object that acts as a credentialed proxy. Before any call goes out, the DO classifies the generated code as read or write by inspecting the method and body. Read operations are proxied directly. Write operations are blocked until you explicitly approve them through the elicitation gate. API keys are never present in the generated code — they're held inside the DO and injected server-side when the upstream call is made. The security boundary isn't just a sandbox that gets thrown away; it's a permission architecture that structurally prevents writes from happening without your approval.

The MCP permission system

Agent Lee connects to Cloudflare's own MCP server, which exposes two tools: a search tool for querying API endpoints and an execute tool for writing code that performs API requests. This is the surface through which Agent Lee reads your account and, when you approve, writes to it.

Write operations go through an elicitation system that surfaces the approval step before any code executes. Agent Lee cannot skip this step. The permission model is the enforcement layer, and the confirmation prompt you see is not a UX courtesy. It's the gate.

Built on the same stack you can use

Every primitive Agent Lee is built on is available to all our customers: Agents SDK,