AI intelligence

AI has really changed the game around software development. More people are leveraging AI than ever to contribute patches to projects they use. To me, this is a good thing as more folks will contribute patches rather than fork or not fix them. The main problem is that AI has made generating code fast but there has been very little improvement in maintaining code bases. In this post, we will highlight the ways the Kubernetes community is adapting to the world of AI assisted coding. The first step of this journey was to develop an AI policy. This seems mundane and bureaucratic but there were many PRs that derailed into discussions around AI usage. The AI policy helps steer the conversation around the project's stance on AI and provides a clear signal to contributors on how to use these tools responsibly.

The latest release of the Agents SDK ↗︎ makes it easier to run long work in the background, drive turns through one entry point, and keep chat agents working through deploys, evictions, and reconnects. This release adds first-class detached (background) sub-agent runs with live progress and durable milestones, a single runTurn turn-admission entry point, and a large round of recovery and reliability fixes that continue converging @cloudflare/think and @cloudflare/ai-chat onto one model. Background sub-agents with progress and milestones runAgentTool can now dispatch a sub-agent without blocking the calling turn. A detached run returns a handle immediately and is owned by a durable, eviction-surviving backbone instead of being abandoned when the dispatching turn ends.

AI agents can now deploy Workers to Cloudflare without first requiring a user to sign up, open a browser-based OAuth flow, click through the dashboard, or create an API token. When an agent tries to deploy without Cloudflare credentials, Wrangler can tell it to rerun with --temporary, then deploy the Worker to a temporary preview account. To try this with your agent, update to Wrangler 4.102.0 or later, make sure you are logged out (wrangler logout), and then ask your agent to build something and deploy it to Cloudflare. The agent should follow Wrangler's output and deploy using the --temporary flag. wrangler deploy --temporary The temporary deployment stays live for 60 minutes. During that window, the agent can verify the Worker, redeploy changes, and return both the live Worker URL and claim URL.

The latest release of the Agents SDK ↗︎ makes it easier to build agents that can safely interact with real systems and keep working through interruptions. Agents can now browse websites through Browser Run, write code against external tools through Code Mode, use client-provided tools when delegating to Think sub-agents, and recover more reliably from deploys, Durable Object evictions, and connection churn. Safer browser automation Agents can now use Browser Run through a single durable browser_execute tool. Instead of choosing from a fixed list of actions, the model writes code against the Chrome DevTools Protocol (CDP) and can inspect pages, capture screenshots, read rendered content, debug frontend behavior, and interact with live browser sessions.

We are excited to announce GLM-5.2 on Workers AI, Z.ai's flagship agentic coding model. @cf/zai-org/glm-5.2 is a text generation model built for agentic coding workflows. With function calling and reasoning support, it can handle long codebases, multi-step planning, and tool-augmented agents. Key features and use cases: Agentic coding: Designed for autonomous coding tasks, long-horizon planning, and complex software engineering workflows Large context window: GLM-5.2 supports up to a 1,048,576 token context window.

AI Gateway logs now capture the user agent of the client that made each request, making it easier to identify which SDK, library, or application sent the traffic flowing through your gateway. For example, you can tell apart requests coming from openai-python versus a custom application or a Cloudflare Worker. The user agent appears alongside the other details in each log entry, and you can filter logs by user agent (equals, does not equal, or contains) in the dashboard. For more information, refer to Logging.

@cf/moonshotai/kimi-k2.7-code is now available on Workers AI. Kimi K2.7 Code is a code-optimized variant of the Kimi K2 family, built on a Mixture-of-Experts architecture with 1T total parameters and 32B active per token. Improved coding and agent performance K2.7 Code delivers meaningful gains over K2.6 on coding and agentic benchmarks: +21.8% on Kimi Code Bench v2 +11.0% on Program Bench +31.5% on MLS Bench Lite Reasoning efficiency K2.7 Code uses 30% fewer reasoning tokens compared to K2.6, reducing overthinking and lowering inference cost for reasoning-heavy workloads.

AI Gateway now supports spend limits — cost-based budgets that track cumulative dollar spend and block requests when the budget is exceeded. Unlike rate limiting, which caps the number of requests, spend limits track actual cost based on token usage and model pricing. You can scope limits by model, provider, or custom metadata dimensions. For example, give each user a $200/day budget, cap total gateway spend at $10,000/day, or limit a specific model to $50/day per user. Each rule uses a configurable time window with fixed or sliding enforcement. Spend limits work with both Unified Billing and BYOK requests for models with known pricing. For more details, refer to the Spend limits documentation.