325 articles
325 ARTICLES
One coding agent can write a Python Worker and another can write a JavaScript Worker. At runtime, those Workers can exchange references to live objects and call their methods without defining APIs, schemas, or serialization code
The Open Source Technology Improvement Fund is proud to share the results of our security audit of Cortex. Cortex functions as a long-term, multi-tenant scalable open source storage for Prometheus and OpenTelemetry. Thanks to Quarkslab and…
AI agents are only as good as the instructions and context you give them. When we launched Google Agent Skills, our goal was simple: encode Google Cloud domain knowledge into structured, open-source instructions that make AI coding agents significantly smarter, safer, and more accurate. Today, I want to take you behind the scenes of Google Agent Skills. As a team member working directly on these skills, I will share how we started, how we maintain quality at scale, and how we handle governance for public and internal skills. How it started: The Next ‘26 kickoff swarm The Google Agent Skills project didn’t start in a vacuum. It kicked off as a fast-paced “swarm” effort leading up to Google Cloud Next 2026. A cross-functional task force led by Developer Advocates and Technical Writers came together with a clear goal: package Google Cloud domain knowledge into structured, agent-readable instructions. The launch was announced in the official Google Agent Skills launch post. The initial community reception exceeded our expectations with over 15,000 GitHub stars! Once developers and engineering teams inside and outside Google saw how effectively skills guided AI agents (reducing hallucinations and enforcing best practices), many wanted in. Soon, a wave of product teams wanted to contribute skills for their own Google services (not limited to Cloud, i.e. Ads). The challenge: Scaling without losing quality Popularity brings a major challenge: quality control. When different teams contribute skills, keeping a consistent standard becomes tough. A poorly written skill with vague instructions, broken links, or missing edge cases degrades the entire agent experience. To enable teams to publish skills while protecting the developer experience, we had to set a very high bar. This meant the process was critical. Without clear standards and automated governance, an open-source skills repository quickly becomes chaotic. So let’s dive into details of how we maintain quality as we scale. The anatomy of an Agent Skill To keep skills consistent across many Google services, every skill follows a standardized repository layout: Architectural best practice: Prefer remote MCP tools When designing skills, our guiding principle is: Reference remote Model Context Protocol (MCP) tools whenever possible, falling back to CLI or API calls only when necessary. Remote MCP servers are best suited for Agentic workloads by providing tools, while also offering built-in auth and IAM governance. Public export We build and evaluate our skills internally first to make sure they work and are properly validated. Once ready to go public, we use automated export rules to publish to GitHub. This keeps public repos clean while stripping out internal assets, ownership information, and evaluation suites. Automated checks on check-in Before any skill enters the repository, it must pass an automated CI/CD pipeline: Linters: We validate frontmatter metadata, line counts, directory layout, and strict naming conventions. Link Checkers: We test every URL using link-checking tools to eliminate 404s and hallucinated links before merge. AI-Assisted Checklists: We use automated validation checks to verify that instructions follow required structural patterns and guardrails. Continuous evals (on submit & weekly) Documentation and APIs evolve, and so do LLM models and agent harnesses. A skill that works today might break tomorrow if an underlying API, model, or agent harness changes. To set an initial quality bar and prevent degradation, we run continuous evaluations: On-submit evaluations: Authors must provide explicit evaluation prompt suites and scoring rubrics. Every new skill that we launch is first evaluated internally to ensure its accuracy and efficiency. Weekly quality checks: We run continuous, scheduled evaluation jobs against the full skill library to catch regressions early. Skill authors must supply multiple evaluation test cases, each containing a prompt and a set of expectations. With each evaluation suite, we compare the performance of agents with and without each skill. And look at two main dimensions: Accuracy - response quality and task completion rate Efficiency - number of consumed tokens and time for completion Moreover we run our evals multiple times against different agent frameworks to obtain statistically significant results. Finally the 2x2 matrix proves whether a skill delivers a measurable accuracy and efficiency uplift. Skills are products, not snippets A key lesson learned from our work is that a skill is a living product, not a one-off document. To ensure long-term reliability, we established strict ownership rules: Repo maintainers oversee repository health, CI pipelines, and architectural standards. Skill owners are responsible for maintaining their skills long-term. For example, if a product API changes, the skill owner updates the skill. The same applies to quality degradation found during evaluation runs. Supporting authors: Tools and agentic workflows Writing effective instructions and evaluation suites requires practice and we don’t expect skill authors to craft everything from scratch. To support our contributors, we built several tools and agentic workflows: Internal skills designed specifically to assist authors building new skills and writing robust evaluations. Agentic tools built with the ADK that run multi-agent loops for authoring and self-critique, with an easy export path to the main repository. I will dive deeper into these authoring tools and agentic workflows in future articles. Internal efficiency with “DevRel Skills” While Google Agent Skills hosts public skills for external developers, we also launched a parallel internal initiative called DevRel Skills. DevRel Skills focus on building agent skills specifically for internal team workflows. By encoding internal processes — such as content transformation, SEO optimization, internal reporting, etc. - into dedicated skills, we help our team work more effectively and consistently every day. Links and further reading To get started with Google Agent Skills, check out: Google Agent Skills Repo: github.com/google/skills Part 1: Introduction: What Are Google Cloud Agent Skills? Part 2: Practical Guide: Intermediate Agent Skills in Action What’s next? If you found this post helpful: Share this post with your friends on socials. Connect with me via LinkedIn, X or Bluesky. Thanks for reading!
Microsoft’s Agent Framework now ships a supported runtime. Build 2026 brought the Agent Harness, the GitHub Copilot and Claude Agent SDK connectors, and the orchestration patterns to stable release; the harness and Foundry Hosted Agents have since reached GA. The shift is from an SDK for building agents to a governed platform for running them. By Steef-Jan Wiggers
HashiCorp has released a public beta of Vault Kubernetes key management, a KMS v2-compatible plugin that lets the Kubernetes API server delegate envelope encryption to Vault Enterprise, moving the key encryption keys that protect etcd data out of the cluster and into a separately governed trust domain. By Mark Silvester
Visible recognition and support are vital for neurodivergent cyber security professionals to flourish. So, how can leaders ensure they get it right?
Grok’s design and training allows it to generate harmful sexualised content, according to the policy file in its GitHub repository
Controversial supplier Fujitsu was included in major government procurement opportunities in July 2026
Embabel has reached its 1.0 release, providing a framework for AI agents on Java It allows Java and Kotlin developers to define agents as typed domain objects. Built on Spring AI, Embabel supports multiple model providers and combines planning with predefined state machines, offering flexibility for agent workflows. By Erik Costlow
Cyber security experts assess the impact of Claude Mythos Preview and how IT teams and cyber security tools address the threat
If you’ve been in IT for a long time, you’re probably used to seeing everything you worked on disappear in the face of technological progress and innovation. Change truly is the only constant
Agents Week explores how cloud infrastructure must evolve to serve autonomous agents rather than human browsers. Join us as we unpack the storage, execution, and security primitives needed for an agent-native web.