Docker Sandboxes: Run Agents in YOLO Mode, Safely

Agents have crossed a threshold.

Over a quarter of all production code is now AI-authored, and developers who use agents are merging roughly 60% more pull requests. But these gains only come when you let agents run autonomously. And to unlock that, you have to get out of the way.That means letting agents run without stopping to ask permission at every step, often called YOLO mode.

Doing that on your own machine is risky. An autonomous agent can access files or directories you did not intend for it to touch, read sensitive data, execute destructive commands, or make broad changes while trying to help.

So yes, guardrails matter, but only when they’re enforced outside the agent, not by it.  Agents need a true bounding box: constraints defined before execution and clear limits on what it can access and execute. Inside that box, the agent should be able to move fast.

That’s exactly what Docker Sandboxes provide.

They let you run agents in fully autonomous mode with a boundary you define. And Docker Sandboxes are standalone; you don’t need Docker Desktop. That dramatically expands who can use them. For the newest class of builder, whether you’re just getting started with agents or building advanced workflows, you can run them safely from day one.

Docker Sandboxes work out of the box with today’s coding agents like Claude Code, Github Copilot CLI, OpenCode, Gemini CLI, Codex, Docker Agent, and Kiro. They also make it practical to run next-generation autonomous systems like NanoClaw and OpenClaw locally, without needing dedicated hardware like a Mac mini.

Here’s what Docker Sandboxes unlock.

You Actually Get the Productivity Agents Promise

The difference between a cautious agent and a fully autonomous one isn’t just speed. The interaction model changes entirely. In a constrained setup, you become the bottleneck: approving actions instead of deciding what to build next. In a sandbox, you give direction, step away, and come back to a cloned repo, passing tests, and an open pull request. No interruptions. That’s what a real boundary makes possible.

You Stop Worrying About Damage

Running an agent directly on your machine exposes everything it can reach. Mistakes are not hypothetical. Commands like rm -rf, accidental exposure of environment variables, or unintended edits to directories like .ssh can all happen.

Docker Sandboxes offer the strongest isolation environments for autonomous agents. Under the hood, each sandbox runs in its own lightweight microVM, built for strong isolation without sacrificing speed. There is no shared state, no unintended access, and no bleed-through between environments. Environments spin up in seconds (now, even on Windows), run the task, and disappear just as quickly. 

Other approaches introduce tradeoffs. Mounting the Docker socket exposes the host daemon. Docker-in-Docker relies on privileged access. Running directly on the host provides almost no isolation. A microVM-based approach avoids these issues by design. 

Run Any Agent

Docker Sandboxes are fully standalone and work with the tools developers already use, including Claude Code, Codex, GitHub Copilot, Docker Agent, Gemini, and Kiro. They also support emerging autonomous systems like OpenClaw and NanoClaw. There is no new workflow to adopt. Agents continue to open ports, access secrets, and execute multi-step tasks. The only difference is the environment they run in. Each sandbox can be inspected and interacted with through a terminal interface, so you always have visibility into what the agent is doing.

What Teams Are Saying

“Every team is about to have their own team of AI agents doing real work for them. The question is whether it can happen safely. Sandboxes is what that looks like at the infrastructure level.” — Gavriel Cohen, Creator of NanoClaw

“Docker Sandboxes let agents have the autonomy to do long-running tasks without compromising safety.”— Ben Navetta, Engineering Lead, Warp

Start in Seconds

For macOS: brew install docker/tap/sbx

For Windows: winget install Docker.sbx

Read the docs to learn more, or get in touch if you’re deploying for a team. If you’re already using Docker Desktop, the new Sandboxes experience is coming there soon. Stay tuned.

What’s Next

You already trust Docker to build, ship, and run your software. Sandboxes extend that trust to agents, giving them room to operate without giving them access to everything.

Autonomous agents are becoming more capable. The limiting factor is no longer what they can do, but whether you can safely let them do it.

Sandboxes make that possible.
Quelle: https://blog.docker.com/feed/

Run and Iterate on LLMs Faster with Docker Model Runner on DGX Station

Back in October, we showed how Docker Model Runner on the NVIDIA DGX Spark makes it remarkably easy to run large AI models locally with the same familiar Docker experience developers already trust. That post struck a chord: hundreds of developers discovered that a compact desktop system paired with Docker Model Runner could replace complex GPU setups and cloud API calls.

Recently at NVIDIA GTC 2026, NVIDIA is raising the bar with NVIDIA DGX Station and we’re excited to add support for it in Docker Model Runner!  The new DGX Station brings serious performance, and Model Runner helps make it practical to use day to day. With Model Runner, you can run and iterate on larger models on a DGX Station, using the same intuitive Docker experience you already know and trust.

From NVIDIA DGX Spark to DGX Station: What has changed and why does this matter?

NVIDIA DGX Spark, powered by the GB10 Grace Blackwell Superchip, gave developers 128GB of unified memory and petaflop-class AI performance in a compact form factor. A fantastic entry point for running models.

NVIDIA DGX Station is a different beast entirely. Built around the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, it connects a 72-core NVIDIA Grace CPU and NVIDIA Blackwell Ultra GPU through NVIDIA NVLink-C2C, creating a unified, high-bandwidth architecture built for frontier AI workloads. It brings data-center-class performance to a deskside form factor. Here are the headline specs:

DGX Spark (GB10)

DGX Station (GB300)

GPU Memory

128 GB unified

252 GB

GPU Memory Bandwidth

273 GB/s

7.1 TB/s

Total Coherent Memory

128 GB

748 GB

Networking

200 Gb/s

800 Gb/s

GPU Architecture

Blackwell (5th-gen Tensor Cores, FP4)

Blackwell Ultra (5th-gen Tensor Cores, FP4)

With 252GB of GPU memory at 7.1 TB/s of bandwidth and a total of 748GB of coherent memory, the DGX Station doesn’t just let you run frontier models,  it lets you run trillion-parameter models, fine-tune massive architectures, and serve multiple models simultaneously, all from your desk.

Here’s what 748GB of coherent memory and 7.1 TB/s of bandwidth unlock in practice:

Run the largest open models without quantization. DGX Station can run the largest open 1T parameter models with quantization.

Serve a team, not just yourself. NVIDIA Multi-Instance GPU (MIG) technology lets you partition NVIDIA Blackwell Ultra GPUs into up to seven isolated instances. Combined with Docker Model Runner’s containerized architecture, a single DGX Station can serve as a shared AI development node for an entire team — each member getting their own sandboxed model endpoint.

Faster iteration on agentic workflows. Agentic AI pipelines often require multiple models running concurrently — a reasoning model, a code generation model, a vision model. With 7.1 TB/s of memory bandwidth, switching between and serving these models is dramatically faster than anything a desktop system has offered before.

Bottom line: The DGX Spark made that fast. The DGX Station makes it transformative. And raw hardware is only half the story. With Docker Model Runner, the setup stays effortless and the developer experience stays smooth, no matter how powerful the machine underneath becomes.

Getting Started: It’s the Same Docker Experience

For the full step-by-step walkthrough check out our guide for DGX Spark. Every instruction applies to the DGX Station as well.

NVIDIA’s new DGX Station puts data-center-class AI on your desk with 252GB of GPU memory, 7.1 TB/s bandwidth, and 748GB of total coherent memory. Docker Model Runner makes all of that power accessible with the same familiar commands developers already use on the DGX Spark. Pull a trillion-parameter model, serve a whole team, and iterate on agentic workflows. No cloud required, no new tools to learn.

How You Can Get Involved

The strength of Docker Model Runner lies in its community, and there’s always room to grow. To get involved:

Star the repository: Show your support by starring the Docker Model Runner repo.

Contribute your ideas: Create an issue or submit a pull request. We’re excited to see what ideas you have!

Spread the word: Tell your friends and colleagues who might be interested in running AI models with Docker.

Learn More

Read our original post on Docker Model Runner + DGX Spark 

Check out the Docker Model Runner General Availability announcement

Visit our Model Runner GitHub repo

Get started with a simple hello GenAI application

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

AWS DevOps Agent is now generally available

Now generally available, AWS DevOps Agent is your always-available operations teammate that resolves and proactively prevents incidents, optimizes application reliability and performance, and handles on-demand SRE tasks across AWS, multicloud, and on-prem environments. Building on the preview launch, DevOps Agent now adds new use cases, broader integrations, enhanced intelligence, and enterprise-ready features, including the ability to investigate applications in Azure and on-prem environments, add custom agent skills to extend capabilities, and create custom charts and reports for deeper operational insights. DevOps Agent investigates incidents and identifies operational improvements as an experienced teammate would: by learning your applications and their relationships, working with your observability tools, runbooks, code repositories, and CI/CD pipelines, and correlating telemetry, code, and deployment data. It autonomously triages incidents and guides teams to rapid resolution, reducing mean time to resolution (MTTR) from hours to minutes, while analyzing patterns across historical incidents to deliver actionable recommendations that prevent future outages. For the full list of AWS Regions where AWS DevOps Agent is available, visit the Regions list. Pricing details are available on the AWS DevOps Agent pricing page. AWS Support customers receive monthly DevOps Agent credits based on the prior month’s gross AWS Support spend: 100% for Unified Operations, 75% for Enterprise Support, or 30% for Business Support+. For many customers, this significantly reduces or eliminates DevOps Agent costs. For details, visit the support compare page. If you are a preview customer, review the migration documentation to ensure seamless access to new AWS DevOps Agent capabilities. To learn more, read the launch blog and see getting started.
Quelle: aws.amazon.com

Amazon ECS Managed Instances now supports Amazon EC2 instance store

Amazon ECS Managed Instances now supports Amazon EC2 instance store volumes as a data volume option for container workloads. You can now leverage instance store volumes on your ECS container instances instead of provisioning an Amazon EBS data volume, reducing storage costs and accelerating I/O performance for latency-sensitive workloads. Amazon ECS Managed Instances is a fully managed compute option designed to eliminate infrastructure management overhead, dynamically scale EC2 instances to match your workload requirements, and continuously optimize task placement to reduce infrastructure costs. With today’s launch, you can enable local storage by configuring a custom ECS Managed Instances capacity provider and selecting the desired Amazon EC2 instance types that include instance store volumes. When an instance lacks instance store volumes or when local storage is disabled, Amazon ECS automatically provisions an Amazon EBS data volume. Support for instance store is available in all commercial AWS Regions where Amazon ECS Managed Instances is available. To learn more about local storage support, visit the documentation. To learn more about Amazon ECS Managed Instances, visit the feature page, documentation, and AWS News launch blog.
Quelle: aws.amazon.com

AWS Service Availability Updates

We’re announcing availability changes to the following AWS services and features. Services moving to Maintenance
Services moving to maintenance will no longer be accessible to new customers starting April 30, 2026. Customers already using these services and features can continue to do so. AWS will continue to operate and support these services and features. We recommend that customers learn about the changes in the product pages and documentation.

Amazon Application Recovery Controller (ARC) – Readiness Check Feature
Amazon Comprehend – Topic Modeling, Event Detection, and Prompt Safety Classification Features
Amazon Rekognition – Streaming Events and Batch Image Content Moderation Features
Amazon Simple Notification Service (SNS) – Message Data Protection (MDP) Feature
AWS App Runner
AWS Audit Manager
AWS CloudTrail Lake
AWS Glue – Ray Jobs Feature
AWS IoT FleetWise

Services entering Sunset
The following services are entering sunset, and we are announcing the date upon which we will end operations and support of the service. Customers using these services should click on the links below to understand the sunset timeline and begin planning migration to alternatives as recommended in the updated service web pages and documentation.

Amazon RDS Custom for Oracle
Amazon WorkMail
Amazon WorkSpaces Thin Client
AWS Service Management Connector

Services reaching End of Support
The following feature has reached end of support and is no longer available as of March 31, 2026.

Amazon Chime SDK – Proxy Sessions

For customers affected by these changes, we’ve prepared comprehensive migration guides, and our support teams are ready to assist with your transition. Visit AWS Product Lifecycle Page to learn more, and subscribe to the RSS feed for future updates. 
Quelle: aws.amazon.com

Aurora DSQL launches new connectors that simplify building .NET and Rust applications

Today we are announcing the release of Aurora DSQL connectors for .NET (Npgsql) and Rust (SQLx) that make it easy to build .NET and Rust applications on Aurora DSQL. The connectors streamline authentication and eliminate security risks associated with traditional user-generated passwords by automatically generating tokens for each connection, ensuring valid tokens are always used while maintaining full compatibility with existing Npgsql and SQLx features. The connectors handle IAM token generation, SSL configuration, and connection pooling, enabling customers to scale from simple scripts to production workloads without changing their authentication approach. They also provide opt-in optimistic concurrency control (OCC) retry with exponential backoff, custom IAM credential providers, and AWS profile support, making it easier to develop client retry logic and manage AWS credentials. To get started, visit the Connectors for Aurora DSQL documentation page. For code examples, visit our GitHub pages for the .NET connector and Rust connector. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage.
Quelle: aws.amazon.com

AWS Deadline Cloud now supports new fleet scaling configurations for render farms

Today, AWS Deadline Cloud introduces three powerful new fleet scaling options that give you greater flexibility in managing your render farm capacity and performance: worker idle duration, standby worker count, and scale out rate. AWS Deadline Cloud is a fully managed service that helps creative teams efficiently manage and scale their rendering workloads in the cloud. These new options give you direct control over balancing rendering speed and efficiency. Configurable worker idle duration allows you to specify how long workers remain available after completing a job, eliminating wait times between job submissions and speeding up artist’s iteration workflow. Standby worker count maintains a pool of pre-warmed, idle workers that are immediately available at job submission so your renders start right away. Scale out rate lets you configure how quickly your fleet scales, up to 500 workers per minute, giving you the control you need to match your infrastructure needs.
These flexible scaling controls are now available in AWS Deadline Cloud. To learn more, visit the AWS Deadline Cloud documentation.
Quelle: aws.amazon.com