Docker and CNCF partner on an open spec for agent permissions

Docker and CNCF: Making what an agent may do as portable as the agent itself

Ten years ago, the software industry faced a choice. Every vendor could ship its own image format and its own runtime, and developers would have to pick a side. Or the industry could agree on one artifact. The second option won. Docker donated its image format and the Runc runtime to the Linux Foundation, and the Open Container Initiative (OCI) formed around them. Today, a Docker image built anywhere can run anywhere. The format is the backbone of the cloud native ecosystem and a de facto standard.

Now, we see a similar problem forming around agents. There is no shared format for what an agent is allowed to do. We are proposing the same kind of answer: one artifact, built on OCI, governed in the open.

The same problem, for agents

Containers were built for immutable software. The image is the application. If you want to change it, you rebuild it, and it behaves the same way everywhere. That’s why a container image describes how software is built and says nothing about what it may do once it runs. For a web service, that was fine. It got a network and a port, and that was enough.

Agents are mutable by definition. Claude Code and Codex install packages, call APIs, and use credentials on your behalf. They change the environment they run in, and they decide what to do next. So, every team writes its own rules for what an agent may reach: a network rule here, a token there, a volume mount to get a task done. Those rules live in shell history, in dashboards, and in someone’s memory. A few months in, nobody can answer a simple question: what is this agent allowed to do?

Every team answers that on its own today, and every runtime vendor could ship its own way of answering it. That’s the kind of fragmentation OCI was created to prevent.

What we are announcing

Kits are not new. Kits have been part of Docker Sandboxes as the way you package an agent, its tools, and what it may reach into something a team can share. What’s new is the artifact. A Kit is now an ordinary OCI image, and the format that describes it is open.

Today at WeAreDevelopers, we announced the Docker Sandbox Kit Spec, open source under Apache 2.0. A Kit carries three things in one image: the agent, its tools, and a typed list of everything it asks to reach, such as hosts, credentials, and volumes. Because the list is part of the image, pinning the image pins the agent and its requests together.

A Kit is not a new artifact type and not a fork of any OCI specification. It uses an extension point OCI already defines. It builds, pushes, pulls, signs, and scans like any image you run today, because it is one.

Today, we’re bringing the spec to CNCF, under their neutral governance, just like we did when the image format went to OCI.

Why it matters

Adoption is free. Because a Kit is an OCI image, every registry, scanner, and signing tool you already run handles it. There is nothing new to deploy.

The answer travels with the agent. Because the requests are in the image, “what may this agent do” has one answer. A teammate can pull it. A reviewer can diff it. A conforming runtime can enforce it. When a new version asks for more, the change shows up as added lines someone can refuse.

An ecosystem, not a format

Docker has always had an ecosystem-first mindset. The Dockerfile mattered because anyone could write one, any registry could store the result, and any runtime could run it. Kits follow the same approach, and we did not build them alone.

We have worked with AWS, Box, Datadog, Dynatrace, JFrog, NanoClaw, OpenClaw, Palo Alto Networks, Snyk, and more to build Kits for their tools. Cloud platforms, observability, security, artifact management, content, and agent frameworks are all represented. The Kits we unveiled during the opening keynote at WeAreDevelopers today are the product of all that work, and they are the first of many.

MCP gave agents a standard way to talk to a tool. Kits give the ecosystem a standard way to publish the whole arrangement: the agent, its tools, and what it asks to reach, in one image anyone can pull. That’s what turns a format into a supply chain. For example, a database vendor can publish a Kit that connects any agent to its service with the scope it recommends.An agent maker publishes its own Kit, so the request list comes from the people who built the agent. A platform team publishes one for the company, and every engineer starts from the same place.

None of that happens if the format belongs to one vendor. A standard for deciding what an agent may do is worth a good deal less if it belongs to whoever sells you the runtime. Docker Sandboxes is the first runtime that enforces it. It should not be the only one, and under CNCF governance, it will not be.

Containers made software portable. Kits make authority portable: the set of things you deliberately hand over to an agent travels with the agent, in the same image, and means the same thing wherever a conforming runtime opens it.

“Standards are what let an ecosystem move fast without fragmenting, and few companies understand that better than Docker. By delivering Sandbox Kits as standard OCI images, Docker is giving the industry an open, repeatable way to package an AI agent, its tools, and its guardrails as one artifact. OCI is the foundation the cloud native ecosystem is built on, so a standard for agents that builds on OCI reaches the whole ecosystem at once. The CNCF welcomes this, and we’re excited to work with Docker and the community on making it broadly adopted.“

Chris Aniszczyk
CTO at CNCF

Build a Kit

If you make a tool agents use, publish a Kit for it. If you run agents, start from one and share it with your team. The specification, the capability pages, and a worked tour of a real Kit are at docker/sandbox-kit-spec. If there is a Kit you cannot express, or a rule a runtime cannot implement, open an issue. Every Kit published and every issue filed is how a standard gets built.

Quelle: https://blog.docker.com/feed/

Introducing Cloud Sandboxes: Start on Your Laptop, Finish in the Cloud

Run agents on your laptop, in the cloud, and move between them with one command, all safely. Earlier this year, we launched Docker Sandboxes: microVM environments where coding agents can work autonomously and safely. 

Since then, agents have started taking on long-horizon work: tasks that run for hours, not minutes. Agents need a new place to work. Today, we’re introducing Cloud Sandboxes: the same microVM-based sandbox, running on Docker-managed compute, with one command to move between them. 

The most important change in coding agents over the past year is that they can work for much longer. Tasks that used to need a developer checking in every few minutes (a large refactor, a dependency migration, a test suite that takes an hour) you can now hand off and review when they finish.

When agents worked in short bursts, the question was whether the model could hold a task together. Now that they work in hours, the question is where those hours happen. A laptop is built around a person. It sleeps when the lid closes, slows down on battery, and disconnects when you move. None of that matters for a task that takes thirty seconds. All of it matters for a task that takes all night.

We built Docker Sandboxes to answer the first question about agents: is it safe to run unattended? In Docker Sandboxes, agents run in a microVM with its own kernel and Docker daemon, isolated from your machine, so they can work autonomously while your files, network, and secrets are protected if an agent steps out of line.

Today, we’re introducing Cloud Sandboxes to answer the second question: how do I run a dozen agents at once, for five, ten, or 21 hours each, without watching any of them? Cloud Sandboxes is the same microVM, running on Docker-managed compute. The isolation model is identical. The CLI is identical. What changes is that the machines underneath are always on, and there are as many of them as you need.

Here’s what that means for you:

Close your laptop and keep the work going. Start an agent in the cloud before you leave for the day, disconnect, and review what they did in the morning.

Start local and move to the cloud when your task outgrows your laptop. Iterate with an agent on the code in front of you, then hand off long tasks and loops to a background agent.

$ sbx move my-project –to cloud

A move captures the sandbox’s filesystem and recreates it on the other side, so your work carries over. This works in both directions.

Run 100 tasks in parallel with nothing to provision. Docker runs the compute. You point your agents at it. Each one gets its own microVM, its own secrets, and its own network policy.

Try things cheaply. Run a pre-built agent for an hour to test an idea, then spin it down.

Everything an agent needs to run on its own

Agents that do long-horizon work without you need three things:

A way to start with nothing to install

Tools to do their jobs

Limits on what it can reach

Cloud Sandboxes ship with all three. You can spin up and manage Cloud Sandboxes from the CLI or from the web console.

Kits. A kit is a pre-configured, pre-built sandbox for an agent. The leading coding agents are ready today, including Claude Code, Codex, Copilot, Antigravity, Open Code and Hermes. Or, you can easily add your own. It’s easy to spin up. sbx –cloud run codex starts one, for example. 

MCP. Connect the MCP servers your agents need (Jira, Linear, Grafana, incident.io, or any streamable HTTP endpoint) once, and all your agents can reach them through a single gateway whether they’re running in the cloud, locally, or in other clients like the ChatGPT desktop app.

Secrets. Store keys or tokens once. Cloud Sandboxes proxy injects it per request, so agents don’t see the actual secret. Prompt injections can’t touch secrets your agents never had in the first place.

Policies. Set network policies on what endpoints agents can access by defining them once. Centralized governance is coming soon for enterprises through Docker AI Governance.

You can access and manage all of this through the web console.

Why one sandbox in two places

We could have built Cloud Sandboxes as a separate product with its own commands. We didn’t, and the reason is simple: how much you can trust your agent shouldn’t depend on where it happens to be running.

As far as we know, no other agent sandbox works this way. We think the right answer to using agents is one strong isolation model that runs in both surfaces (locally and in the cloud), and an easy way (one command) to move between them.

These aren’t two tiers of one product. Interactive work belongs on your laptop. Work that takes hours belongs in the cloud. Most developers need both, and now you don’t have to pick.

Pricing

Cloud Sandboxes are pay-as-you-go. We meter compute by the second, and nothing else. A paused sandbox costs nothing. Volumes, egress, and hosting public images and Kits are free. You can bring your own model key and keep your existing provider for inference.

Size

vCPUs

Memory

Per hour

Micro

1

2 GiB

$0.07

Small (default)

2

4 GiB

$0.14

Medium

4

8 GiB

$0.28

Large

8

16 GiB

$0.56

XL

16

32 GiB

$1.12

Sandboxes run for one hour by default and up to 24 hours per session.

Get started

From the browser: sign in to the web console, choose a kit, and click Run. 

From the terminal:

$ brew install docker/tap/sbx
$ sbx login
$ sbx –cloud run claude

You’ll need sbx 0.45.1 or later and the pay-as-you-go plan, available on Docker Personal and Pro accounts. Docker Sandboxes on your laptop remain free and standalone, with no Docker Desktop required. Local and cloud sandboxes keep separate secrets, templates, and network policies, so read the differences in the docs before you move a local workflow.

For a limited time, new accounts get $250 in free Cloud Sandboxes credit. Claim it here.

We think the way software gets built is changing. Developers will spend less time typing and more time directing: handing agents real tasks, stepping back, and reviewing what comes back. For that to work, agents need environments that are safe enough to run unsupervised and durable enough to run for hours. That’s what Docker Sandboxes is for, and as of today, it runs on your laptop and in the cloud.

Start one before you close your laptop tonight.
Quelle: https://blog.docker.com/feed/

Manufacturing Trust for AI Agents | Docker’s WeAreDevelopers Keynote

Coding agents do more than write code. Agents can install dependencies, access networks, use credentials, and keep working after we’ve moved on to something else. Agents need access to get real work done. But the more access they have, the bigger the impact of a mistake. The challenge is building enough trust into the system to give agents more freedom without giving them access to everything. 

At WeAreDevelopers North America, Docker President Mark Cavage laid out Docker’s answer: give every agent a strong boundary, make its environment and authority reproducible, and let the work run where it makes sense, from a developer’s laptop to the cloud. 

In 30 minutes, Mark demonstrates what happens when an agent pushes beyond a container’s limits and how Docker Sandboxes create a stronger boundary. He then connects that foundation to Kits, cloud capacity for work that needs to continue beyond the laptop, and new partnerships helping developers put agents to work safely. 

Agents are actors, not workloads

Containers were built to isolate applications. Agents do more. They make decisions and act on the systems around them, so the boundary needs to extend to the environment they can reach. 

Mark’s onstage demo makes that clear. The container worked as designed. What the agent needed was containment around its full environment. Docker Sandboxes provide that containment. Each agent gets its own isolated microVM and kernel, separate from the developer’s host environment. 

Developers can choose the agent, model, and tools that fit the job, then define the sandbox’s access to files, networks, and secrets. Those policies stay outside the agent’s control. Together, the sandbox and its policies create a trusted environment for autonomous work. 

Docker Sandboxes are available today through the free, standalone CLI, so developers can bring stronger isolation to the agent workflows they already use. 

Make the agent and its authority reproducible

A strong boundary gives an agent a safe place to work. Developers also need a repeatable way to define what runs and what it can access. 

In the keynote, Mark announced the next generation of Docker Sandbox Kits, now built as standard OCI images, and invited ecosystem partners to help shape the specification as Docker works towards neutral governance. 

Docker Sandbox Kits package the agent, its tools, and the rules for what the sandbox can reach into one versioned, shareable artifact. Developers can share Kits through the same workflows they already use for container images, while keeping changes to an agent’s authority visible and reviewable. 

The result is a common foundation for the agent ecosystem, built on an open standard. 

Dockerfiles made software reproducible. Kits make authority reproducible

Start on your laptop. Finish in the cloud.

Some agent work begins and ends on a developer laptop. Longer-running or parallel work needs capacity that stays available when the developer steps away. In the keynote, Mark announced Docker Cloud Sandboxes, bringing the same microVM-based isolation from the laptop to Docker-managed cloud compute. 

Using the familiar sbx workflow, developers can start locally, move their work to the cloud with one command, and let it continue after they close their laptop. They can also run tasks in parallel without provisioning or maintaining the infrastructure themselves. 

Docker Cloud Sandboxes are available today with pay-as-you-go pricing.

An ecosystem built around trust

An open agent ecosystem needs both: choice for the developers and shared standards the industry can build on.

Nous Research joined Mark onstage to show Hermes running as a first-class Kit in Docker Sandboxes. The demo showed what choice looks like in practice: a third-party agent packaged for a common environment, with Docker providing the underlying isolation and controls. Developers can choose the agent that best fits their work without having to rebuild the trusted execution layer around it.

Choice also depends on shared standards that the wider ecosystem can adopt. Docker has committed to submitting its Kits specification to the Cloud Native Computing Foundation (CNCF), the open source, vendor-neutral hub of cloud-native computing.

“Standards are what let an ecosystem move fast without fragmenting, and few companies understand that better than Docker through their involvement in efforts like the OCI and CNCF. By delivering Sandbox Kits as standard OCI images, Docker is giving the industry an open, repeatable way to package an AI agent, its tools, and its guardrails as one artifact. OCI is the foundation the cloud native ecosystem is built on, so a standard for agents that builds on OCI reaches the whole ecosystem at once. The cloud native community looks forward to working with Docker to bring this work under neutral governance.” 

Chris Aniszczyk
CTO at CNCF

Together, these partners show what an open agent ecosystem can look like: choice at the agent layer and a shared format for packaging an agent’s environment and authority. Docker provides the trusted foundation underneath, whether agents run locally or in the cloud.

Trust comes from the system around the agent

Taken together, the announcements in Mark’s keynote form one system. Sandboxes provide a deterministic boundary. Kits make the agent’s environment and authority reproducible. Cloud Sandboxes extend the same trust model to durable cloud capability. 

They give developers the freedom to choose their agents and more autonomy, while retaining control over what they can access and change. 

As agents take on bigger jobs, the infrastructure around them matters more. Docker provides the trusted foundation they need to work locally or scale in the cloud, while developers continue to stay in control of what ships.  

Ready to try Cloud Sandboxes?

For a limited time, new accounts can claim $250 in compute credit to get started.

Watch, Explore, Build

Start Locally: Run an agent in a Docker Sandbox 

Scale in the Cloud: Read the announcement and get started with Docker Cloud Sandboxes 

Build with Kits: Read the technical deep dive and explore the open kit specification. 

Quelle: https://blog.docker.com/feed/

Amazon RDS for PostgreSQL now supports PostgreSQL 19 Beta 4 in the Amazon RDS Database Preview Environment

Starting today, Amazon RDS for PostgreSQL 19 Beta 4 is available in the Amazon RDS Database Preview Environment, allowing you to evaluate the pre-release of PostgreSQL 19 on Amazon RDS for PostgreSQL.
PostgreSQL 19 Beta 4 refines the query performance and autovacuum management capabilities introduced in earlier releases and includes bug fixes and stability improvements since Beta 3. Fixes include pg_stat_autovacuum_scores view reporting for TOAST tables and corrected freeze-score scaling so you can tune autovaccum prioritization effectively. Parallel autovacuum now shares a rebalanced cost limit across its workers to keep maintenance on large tables within your configured limits. Refer to the PostgreSQL community announcement for more details.
Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots that are created in the Preview Environment can only be used to create or restore database instances within the Preview Environment. You can use the PostgreSQL dump and load functionality to import or export your databases from the Preview Environment. Amazon RDS Database Preview Environment database instances are priced as per the pricing in the US East (Ohio) Region.
Quelle: aws.amazon.com

Amazon RDS for PostgreSQL now supports post-quantum TLS key exchange

Starting today, Amazon RDS for PostgreSQL introduces support for post-quantum TLS (PQ-TLS) key exchange, providing you with post-quantum cryptography options for encrypting your data in-transit.
RDS for PostgreSQL versions 18 and higher support modification of the ssl_groups parameter. You can select cryptographic groups from the RDS for PostgreSQL allow list to align with your organization’s security standards.
Amazon RDS for PostgreSQL makes it simple to set up, operate, and scale PostgreSQL deployments in the cloud. Create or update a fully managed Amazon RDS database in the Amazon RDS Management Console or by using the AWS Command Line Interface (AWS CLI).
Quelle: aws.amazon.com

Amazon RDS for MySQL announces Extended Support minor versions 5.7.44-rds.20260902 and 8.0.46-rds.20260908

Amazon Relational Database Service (RDS) for MySQL announces Amazon RDS Extended Support minor versions 5.7.44-rds.20260902 and 8.0.46-rds.20260908.
Amazon RDS Extended Support provides up to three additional years of fixes for critical Common Vulnerabilities and Exposures (CVE) and bugs beyond a major version’s end of standard support date, giving you more time to upgrade to a new major version.

You can upgrade your database using Amazon RDS Blue/Green Deployments, in-place upgrade, or restore from a snapshot. Learn more about performing version upgrades in the Amazon RDS for MySQL User Guide. You can also migrate to MySQL 8.4 from external MySQL sources using AWS Database Migration Service. Learn more about pricing details and regional availability at Amazon RDS for MySQL.
Amazon RDS for MySQL makes it simple to set up, operate, and scale MySQL deployments in the cloud. Create or update a fully managed Amazon RDS database in the Amazon RDS Management Console or by using the AWS Command Line Interface (AWS CLI).
Quelle: aws.amazon.com

AWS Lambda durable functions are now available in AWS European Sovereign Cloud region

AWS Lambda durable functions are now available in the AWS European Sovereign Cloud. Lambda durable functions enable developers to build reliable multi-step applications and AI workflows within the Lambda developer experience.
Lambda durable functions extend the Lambda programming model with new primitives in your event handler, such as “step” and “wait”, allowing you to checkpoint progress, automatically recover from failures, and pause executions without incurring compute charges for on-demand functions. With availability in the AWS European Sovereign Cloud region, customers can orchestrate complex processes such as order workflows, user onboarding, and AI-assisted tasks while meeting compliance and security requirements.
You can activate durable functions for new Python (versions 3.13 and 3.14), Node.js (versions 22 and 24), or Java (17+) based Lambda functions using the AWS Lambda API, AWS Management Console, or AWS SDK. You can also use infrastructure as code tools such as AWS CloudFormation, AWS Serverless Application Model (AWS SAM), and the AWS Cloud Development Kit (AWS CDK).
For more information on durable functions, visit the AWS Lambda durable functions product page or the AWS Lambda Developer Guide. To learn about pricing, visit AWS Lambda pricing.  
Quelle: aws.amazon.com