HS130: Wait, AI Doesn’t Secure Itself? Developing an AI Security Strategy
Your enterprise better have a cybersecurity strategy for AI. But where to start? Everywhere! Securing AI means securing all the AI layers and throughout the lifecycle: data, model, and applications, in training and in inference. Johna and John discuss what your strategy must address and how to get support for it because, of course, it’s... Read more »
Moving past bots vs. humans
For us humans to interact with the online world, we need a gateway: keyboard, screen, browser, device. What is called "human detection" online are patterns that humans use when interacting with such devices. These patterns have changed in recent years: a startup CEO now uses their browser to summarize the news, a tech enthusiast automates the process to book their concert tickets when sales open at night, someone who's visually impaired enables accessibility on their screen reader, and companies route their employee traffic through zero trust proxies.
At the same time, website owners are still looking to protect their data, manage their resources, control content distribution, and prevent abuse. These problems aren’t solved by knowing whether the client is a human or a bot: There are wanted bots and there are unwanted humans. These problems require knowing intent and behavior. The ability to detect automation remains critical. However, as the distinctions between actors become blurry, the systems we build now should accommodate a future where "bots vs. humans" is not the important data point.
What actually matters is not humanity in the abstract, but questions such as: is this attack traffic, is that crawler load proportional to the traffic it Continue reading
Hmmm: Rail-Optimized Networking for AI Workloads
Phil Gervasi wrote an interesting article describing Rail-Optimized Networking for AI Training Workloads. Go read it first; I’ll wait.
Does it sound interesting? Were you able to see behind the curtain and figure out what it’s really about?
NB571: Linux Loads 7.0 with Network Upgrades; NetGear Routes Around FCC Ban, But How?
Take a Network Break! Our Red Alert covers a trio of vulnerabilities in Cisco ISE. On the news front, Cloudflare announces a private network offering for AI agents and a partnership with CNAPP specialist Wiz for AI visibility. AWS rolls out Interconnect to streamline provisioning of WAN and last-mile connectivity, and Linux 7.0 includes network... Read more »Building the agentic cloud: everything we launched during Agents Week 2026
Today marks the end of our first Agents Week, an innovation week dedicated entirely to the age of agents. It couldn’t have been more timely: over the past year, agents have swiftly changed how people work. Coding agents are helping developers ship faster than ever. Support agents resolve tickets end-to-end. Research agents validate hypotheses across hundreds of sources in minutes. And people aren't just running one agent: they're running several in parallel and around the clock.
As Cloudflare's CTO Dane Knecht and VP of Product Rita Kozlov noted in our welcome to Agents Week post, the potential scale of agents is staggering: If even a fraction of the world's knowledge workers each run a few agents in parallel, you need compute capacity for tens of millions of simultaneous sessions. The one-app-serves-many-users model the cloud was built on doesn't work for that. But that's exactly what developers and businesses want to do: build agents, deploy them to users, and run them at scale.
Getting there means solving problems across the entire stack. Agents need compute that scales from full operating systems to lightweight isolates. They need security and identity built into how they run. They need an agent toolbox Continue reading
The AI engineering stack we built internally — on the platform we ship
In the last 30 days, 93% of Cloudflare’s R&D organization used AI coding tools powered by infrastructure we built on our own platform.
Eleven months ago, we undertook a major project: to truly integrate AI into our engineering stack. We needed to build the internal MCP servers, access layer, and AI tooling necessary for agents to be useful at Cloudflare. We pulled together engineers from across the company to form a tiger team called iMARS (Internal MCP Agent/Server Rollout Squad). The sustained work landed with the Dev Productivity team, who also own much of our internal tooling including CI/CD, build systems, and automation.
Here are some numbers that capture our own agentic AI use over the last 30 days:
3,683 internal users actively using AI coding tools (60% company-wide, 93% across R&D), out of approximately 6,100 total employees
47.95 million AI requests
295 teams are currently utilizing agentic AI tools and coding assistants.
20.18 million AI Gateway requests per month
241.37 billion tokens routed through AI Gateway
51.83 billion tokens processed on Workers AI
The impact on developer velocity internally is clear: we’ve never seen a quarter-to-quarter increase in merge requests to this degree.
As AI Continue reading
Orchestrating AI Code Review at scale
Code review is a fantastic mechanism for catching bugs and sharing knowledge, but it is also one of the most reliable ways to bottleneck an engineering team. A merge request sits in a queue, a reviewer eventually context-switches to read the diff, they leave a handful of nitpicks about variable naming, the author responds, and the cycle repeats. Across our internal projects, the median wait time for a first review was often measured in hours.
When we first started experimenting with AI code review, we took the path that most other people probably take: we tried out a few different AI code review tools and found that a lot of these tools worked pretty well, and a lot of them even offered a good amount of customisation and configurability! Unfortunately, though, the one recurring theme that kept coming up was that they just didn’t offer enough flexibility and customisation for an organisation the size of Cloudflare.
So, we jumped to the next most obvious path, which was to grab a git diff, shove it into a half-baked prompt, and ask a large language model to find bugs. The results were exactly as noisy as you might expect, with a flood Continue reading
SR-MPLS Workshop Materials
We’re starting the SR-MPLS workshop @ ITNOG 10 in Bologna in a few hours. Hope you’ll make it, but if you happen to be too far away, here’s the slide deck, the lab topologies, and the usage guidelines.
Multicast PIM Bootstrap Router (BSR) (VI)
We are at the tail end of our multicast series. So far, we have covered multicast basics, IGMP, PIM Dense Mode, PIM Sparse Mode, and Auto-RP. In this post, we will look at Bootstrap Router, or BSR.
Multicast PIM Auto RP
AutoRP solves this problem by allowing routers to dynamically learn the RP address. Instead of manually configuring the RP on each router
Suresh Vinasiththamby
Bootstrap Router (BSR) Overview
In the previous post, we looked at Auto-RP, which is Cisco's proprietary method for dynamically distributing RP information to all routers in the network. BSR solves the same problem but is defined in the PIM standards (RFC 5059), making it vendor-neutral and interoperable across different platforms. The core idea is the same. Instead of manually configuring the RP address on every router, BSR allows routers to learn RP information automatically. A router called the Bootstrap Router collects RP information from Candidate RPs and distributes it to all PIM routers in the network.
The key difference is in how this information flows. With Auto-RP, the Mapping Agent sends RP mappings to the specific multicast group 224.0.1.40. This creates the chicken-and-egg problem we discussed in the previous post Continue reading
Hedge 302: Communications in Biological Systems
What does biology have to do with computer networks? Much more than you might think. Communications systems, after all, need to solve the same problems–and they often use the same kinds of tools. In this episode of the Hedge, Emily Reeves and Joe Deweese join Russ and Tom to talk about a recent paper comparing computer communications to biological communications.
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HN823: Defining A Modern Network Service
On today’s episode Ethan is joined by Mark Prosser, a self-described Network Operator Advocate and Network Automation Dreamer, to embark on a thought exercise about network services. Together they grapple with questions such as: What is a network service, exactly? How is it defined? Is it even possible to define it when considered in the... Read more »Introducing the Agent Readiness score. Is your site agent-ready?
The web has always had to adapt to new standards. It learned to speak to web browsers, and then it learned to speak to search engines. Now, it needs to speak to AI agents.
Today, we are excited to introduce isitagentready.com — a new tool to help site owners understand how they can make their sites optimized for agents, from guiding agents on how to authenticate, to controlling what content agents can see, the format they receive it in, and how they pay for it. We are also introducing a new dataset to Cloudflare Radar that tracks the overall adoption of each agent standard across the Internet.
We want to lead by example. That is why we are also sharing how we recently overhauled Cloudflare's Developer Documentation to make it the most agent-friendly documentation site, allowing AI tools to answer questions faster and significantly cheaper.
The short answer: not very. This is expected, but also shows how much more effective agents can be than they are today, if standards are adopted.
To analyze this, Cloudflare Radar took the 200,000 most visited domains on the Internet; filtered out categories where agent readiness isn't important Continue reading
Shared Dictionaries: compression that keeps up with the agentic web
Web pages have grown 6-9% heavier every year for the past decade, spurred by the web becoming more framework-driven, interactive, and media-rich. Nothing about that trajectory is changing. What is changing is how often those pages get rebuilt and how many clients request them. Both are skyrocketing because of agents.
Shared dictionaries shrink asset transfers from servers to browsers so pages load faster with less bloat on the wire, especially for returning users or visitors on a slow connection. Instead of re-downloading entire JavaScript bundles after every deploy, the browser tells the server what it already has cached, and the server only sends the file diffs.
Today, we’re excited to give you a sneak peek of our support for shared compression dictionaries, show you what we’ve seen in early testing, and reveal when you’ll be able to try the beta yourself (hint: it’s April 30, 2026!).
Agentic crawlers, browsers, and other tools hit endpoints repeatedly, fetching full pages, often to extract a fragment of information. Agentic actors represented just under 10% of total requests across Cloudflare's network during March 2026, up ~60% year-over-year.
Every page shipped is heavier Continue reading
Agents Week: network performance update
When it comes to the Internet, performance is everything. Every millisecond shaved off a connection is a better experience for the real people using the applications and websites you build. That's why, at Cloudflare, we measure our performance constantly and share updates on a regular basis.
In our last performance post, published during Birthday Week 2025, we shared that Cloudflare was the fastest network in 40% of the largest 1,000 networks in the world. At the time, we noted a nuanced reading of that figure; we were competitive in many more networks, and the gaps were often notably small. But even so, we were not satisfied with 40%. By December 2025 (our most recent available analysis), we had become the fastest provider in 60% of the top networks. Here's how we got there, and what it means.
Before diving into the results, let’s review how we collect the data. We start with the 1,000 largest networks in the world by estimated population, using APNIC's data as our source. These networks represent real users in nearly every geography, giving us a broad and meaningful picture of how Internet users experience the Continue reading
Introducing Flagship: feature flags built for the age of AI
AI is writing more code than ever. AI-assisted contributions now account for a rapidly growing share of new code across the platform. Agentic coding tools like OpenCode and Claude Code are shipping entire features in minutes.
AI-generated code entering production is only going to accelerate. But the bigger shift isn't just speed — it's autonomy.
Today, an AI agent writes code and a human reviews, merges, and deploys it. Tomorrow, the agent does all of that itself. The question becomes: how do you let an agent ship to production without removing every safety net?
Feature flags are the answer. An agent writes a new code path behind a flag and deploys it — the flag is off, so nothing changes for users. The agent then enables the flag for itself or a small test cohort, exercises the feature in production, and observes the results. If metrics look good, it ramps the rollout. If something breaks, it disables the flag. The human doesn't need to be in the loop for every step — they set the boundaries, and the flag controls the blast radius.
This is the workflow feature flags were always building toward: not just decoupling deployment from release, but Continue reading