NAN132: The AI-Augmented Engineer

Garrett Masters talks with Eric Chou about the concept of the AI-Augmented Engineer. Together they discuss how IT professionals can use Python and NetMiko to automate tasks and even connect AI directly to lab environments. Garrett also shares practical advice on prompt engineering, the importance of context management, and how engineers can safely adopt AI... Read more »

TCG085: Enterprise Salescraft with Drew Meli

William Collins and guest Drew Meli explore the realities of buying enterprise SaaS and infrastructure. They cover the common engineering misconceptions about enterprise sales, navigating rigorous security reviews, establishing clear proof of value criteria, and how AI is reshaping buying behaviors. AdSpot Sponsor: Gartner AI is rewriting your cloud architecture, your operating model, and your... Read more »

Managing Claude Code Sessions Through Lynx

At Tigera, we spend a lot of time thinking about agent security: identity, policy, runtime controls, and the record left behind after an agent acts.

Coding agents create an interesting problem because, in most organizations, they didn’t arrive through the front door.

Few companies ran a platform evaluation and rolled Claude Code out to 500 developers. Developers installed it themselves. By the time security and platform teams started asking how coding agents should be governed, they were already running on laptops with access to source code, credentials, SSH keys, kubeconfigs, internal services, and whatever else the developer could reach.

The long-term answer is increasingly clear I think: move coding agents into isolated environments you control.

Anthropic’s sandboxing work draws filesystem and network boundaries using OS primitives such as bubblewrap and seatbelt. Its reference devcontainer includes an egress firewall. Docker has introduced sandboxes for running coding agents, and Kubernetes-based approaches can add stronger workload isolation, network policy, and disposable development environments.

That direction makes sense.

Isolation governs what an agent can do. A gateway governs what it can send.

And unlike a complete move to remote development environments, the second boundary is something you can introduce today.

Start with one environment Continue reading

Cloudflare Impact reaches $100 million in donations

This week, Cloudflare's Impact programs will reach $100 million in donated services. It's a significant milestone, and one that we are proud of because it means that thousands of organizations, like journalism outlets, civil society, state and local governments, election management bodies, and public schools are being protected from cyberattacks.

But Cloudflare's Impact programs have never been about philanthropy. They are a fundamental part of our business and our mission, and they continue to help guide almost everything we do.

As we celebrate this milestone and our 16th Birthday Week, we wanted to revisit not only how we got here, but also how our Impact programs continue to grow and evolve to help those working for the public interest.

Free → Impact

Cloudflare started as a free service. The original idea was to provide a basic version of our services to developers and small businesses for free, and then use the data about cyberattacks on their websites to build more sophisticated products that we could sell.

However, we quickly discovered that some of our free customers were not only doing essential work, like reporting on corruption in Africa or on the Russian invasion of Crimea, but also experiencing some of Continue reading

Cut your AI spend with AI Gateway’s Auto Router

From our conversations with companies at every stage of their AI adoption journey, we've seen some common patterns. First, there is an exploration period as you bring on every new tool, dole out API keys freely, and let the tokens flow. Then, you converge on the canonical tools for your organization for agentic coding, for non-technical workflows, for running and deploying agents. As companies formalize their AI adoption, they want to manage and oversee token spend for users, but budgets and rules only go so far. The best savings are the ones users never notice.

Today, we are releasing Cloudflare's Auto Router in public beta, available through AI Gateway. Set your model to cloudflare/auto and the Auto Router will automatically route each request to a model that is capable enough for the task, without requiring an end user to think about model selection. Our early results using the Auto Router internally through our OpenCode harness show a cost savings of up to 30% when compared to using only frontier models like OpenAI Sol and Anthropic Claude Opus.

Why we built this

From our own experience tracking AI spend at Cloudflare, we’ve learned managing costs requires a multipronged approach. Previously, we talked about how to set Continue reading

Identify AI model overuse with User Insights

When we launched User Insights last month, we wanted to help teams answer a basic question: What are people actually doing with AI? User Insights gives teams a clearer view of their AI usage, showing which users, applications, tasks, and models are driving traffic. It also highlights user and agent anomalies, helping teams identify unexpected or out-of-control spending and usage before they become larger problems.

Our latest update adds something our users have been asking for: context. 

Since launch, we’ve heard from users that model names and request counts only tell part of the story. They show where traffic is going, but reveal little about the work behind it: is that request a code review, a research task, or an agent making several calls to complete a job? The same token count can represent very different kinds of work, and you can’t evaluate with model choice without understanding the task.

User Insights now shows when a model may be more capable than a task requires, which of your users and agents are driving that usage, and how the task, model, cost, and conversation patterns relate. Teams can use these insights to investigate and make targeted changes within their organization. Continue reading

Detect and send production issues straight to your agent

As agents help us build more complex applications, both humans and agents need a better way to stay on top of what goes wrong in production. Coding agents can already query observability data, navigate a repository, change code, write tests, and open a pull request. What remains manual is connecting those steps: recognizing that repeated failures come from the same bug, gathering the relevant logs and traces, sending that context to an agent, and checking whether the fix worked. Without that structured handoff, the agent must search raw telemetry to reconstruct the scope and context of the failure before it can investigate.

Today, we are introducing Issues, built-in error monitoring for Cloudflare Workers (now in open beta!) to streamline this workflow. Issues can:

  • Group repeated exceptions, 5xx responses, and error logs into one issue.
  • Send the error, stack trace, logs, traces, and Worker version to a configured coding agent.
  • Trigger the agent’s configured workflow — from triaging an issue to querying more data to opening a pull request.

Fix your first issue with the following prompt for your agent with CF CLI or checkout the documentation to get started:

Simplifying domains for people and agents

You just thought of your next great idea, and buying the right domain feels like the easiest way to make that first bit of progress. Naturally, you open a new tab in your browser, only to find yourself face-to-face with an experience that feels like a budget airline peppering you with add-ons at checkout: Want security? How about a website? Do you want email? You’re just a few minutes into building your next idea, and it doesn’t feel fun anymore.

Launched a decade ago, Cloudflare Registrar has always taken a simpler approach. Domains at cost, transparent pricing, and no unnecessary upsells. But simplicity shouldn’t begin at checkout. It should begin the moment you start looking for the right domain.

Today, we’re bringing that same simplicity to the entire experience of finding and buying a domain. Our new domain search shows every extension we support, responds as quickly as you type, and makes hundreds of possibilities easier to explore through sorting, filtering, and transparent pricing.

And you know what is particularly good at ignoring distractions and staying focused on the destination? An AI agent. We designed Cloudflare Registrar to work naturally with agents through the Registrar API, MCP Continue reading

Monetization Gateway beta: charge AI agents for consumption with HTTP 402

Today, we’re making the Cloudflare Monetization Gateway available as part of a closed beta, and showcasing four customer use cases that are in production today. Since we announced the plan three months ago, we have been working closely with our customers to make the Gateway fast, flexible, and easy to use.

With just a few clicks, the Monetization Gateway allows domain owners to charge agents for access to their website, APIs, MCP tools, or datasets. Request access today in the Cloudflare Dashboard.

Proliferation of agents and machine payments

Today, most software businesses sell their products through subscriptions or prepaid credits. These business models require buyers to make a considerable upfront economic investment, so buyers limit themselves to a few subscriptions that fit into their budget. But this business model doesn’t align itself with how the predominant source of traffic on the Internet — agents — operates.

Agents seek outcomes, whether that’s sourced from a direct question or an implicit elicitation. To achieve an outcome, an agent may visit new sites, call MCP tools, or ingest data feeds. Businesses everywhere want to be in the critical path to help agents get better results. This helps them meet new users where they Continue reading

Pay Per Use: when AI uses your work, you should get paid

AI answer engines read a publisher’s page and hand the reader a summary, so the visit, and the revenue that would come with it, never happens. Most publishers will never sign a licensing deal with the companies that use their work in AI products, and no company can negotiate with millions of sites. The web needs a way to say “yes, if you pay.” Pay Per Use is one way to say it, and it's now in beta.

In July, we outlined our plan for Pay Per Use. Since then, we’ve been working with buyers and content owners to bring it to life. The gist: A buyer offers a price for a specific use of your content. You choose whether to accept. The buyer reports each use, and Cloudflare bills the buyer and pays you. Publishers track usage and earnings in the Cloudflare dashboard. Buyers report usage through a single API.

Pay for the use, not the crawl

AI products fetch far more than they use. A search engine indexes pages it never shows. Charging for every crawl makes the buyer pay before it knows what it needs, and many buyers won't. Paying for use ties the price to Continue reading

Cloudflare Containers, rebuilt to scale agent sandboxes

Today, we’re making Cloudflare Containers more programmable and optimized for agent workloads. Agents don't deploy sandboxes ahead of time. They create sandboxes on demand, for each task, expect them to be ready immediately, and be able to pause and resume. So we rearchitected Containers to meet these requirements: your code can now choose each sandbox's image and instance type at runtime, Containers start 6x faster, and filesystem snapshots are available in public beta.

To make this possible, we’ve rethought the Containers infrastructure from the bottom up. A new scheduling policy moves control over each sandbox into application code, while a redesigned runtime provides a faster path to a running Container. In ComputeSDK’s independent benchmark, median startup fell from just over four seconds to 648 milliseconds, and, in our own preliminary tests, burst testing successfully created hundreds of thousands of containers in seconds.

All of this builds on what has always set Containers on Cloudflare apart: every Container gets its own Durable Object, a persistent, programmable controller running right next to it that manages its lifecycle, outbound traffic, and more. We are bringing more capabilities directly to the native ctx.container API, so the Durable Object can control its Container without Continue reading

The Internet has a second audience

For most of its history, the Internet had one audience that paid the bills: people. We read the articles, saw the ads, and bought the subscriptions. Bots were always there, but they were mostly large, automated operations that didn't view ads, pay for anything, or read in any meaningful sense.

That's changing fast. At the end of 2024, Cloudflare handled an average of 63 million HTTP requests a second. Today, it's almost doubled to 115 million, with peaks above 150 million. Over the past year, daily requests from AI agents on our network grew by more than 1,700%. This year, for the first time, more than half of Internet traffic wasn't human.

The human web didn't shrink to make room. A second audience arrived alongside it: agents, software acting on behalf of people. They sit somewhere between humans and traditional bots. They don't respond to ads, but there's usually a person behind them with a job to get done. For businesses that learn to serve them and capture value from them, agents are additive. For those that don't, they're extractive.

What our customers need hasn't changed: to be discovered, to tell great stories, to build great experiences, and to sell. What's Continue reading

Model Routing, Part 2: Choosing the Scope

Objective In Part 1, I compared ways to choose which model should answer a support request. This post continues that experiment with a second decision: how long should that model choice last—a whole conversation or just one question? The small pilot illustrates a method, not a general recommendation. I compared four policies on six new … Continue reading Model Routing, Part 2: Choosing the Scope →

Model Routing, Part 1: Choosing a Classifier

Objective How do you choose a model router for your own application? I tested six classifiers on a marketplace support agent to compare cost against answer quality. This small study demonstrates the decision process, not a general ranking of products. Both GPT-5 nano and GPT-5 answered 18 support cases: 16 single-turn and two two-turn cases, … Continue reading Model Routing, Part 1: Choosing a Classifier →

Python Generating Configs with Jinja2

Python Generating Configs with Jinja2

If you have ever rolled out a batch of new sites or devices, I hope you can relate to the pain. The config is more or less the same each time, but the hostnames, IPs, and VLANs change. You copy the config from the last site, tweak the values, save it, and move on. It works, but it does not scale, and it is easy to make mistakes.

The way most of us solve this is with templates. You write the config once with placeholders where the values go, and you fill in the placeholders each time you need a new device config. Jinja2 is the tool we use for this in Python. It takes a template, some data, and gives you back a finished config.

In this post, I will walk you through how Jinja2 works, cover the syntax you actually need, and we will end up generating the same config for Cisco IOS, Juniper Junos, and Arista EOS from the same data. We are only going to generate the configs as text in this post. Pushing them to devices is something you already know how to do from the earlier posts.

Installing Jinja2

Jinja2 is not built into Continue reading

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