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.
We believe the future is a multi-model, multi-harness world. And we think it needs a new trust model.
In 1988, Norm Hardy described a problem that had been quietly breaking systems for years: the confused deputy. A program that takes action using its permissions instead of yours.
Today, every AI agent is that deputy. It inherits your authority: Your credentials, your repo access, your ability to call APIs. But its behavior is probabilistic. It might be acting on an instruction found in its environment, on a step it invented, or on a confident wrong answer.
The industry didn’t fix the confused deputy problem by making the deputy itself more careful. They fixed it by moving its authority a layer away. Forty years on, that’s still the answer.
Everyone is converging on the same future
Three facts are pushing the industry toward the same conclusion.
Agents are expensive loops. An agent takes many steps, and you pay for every token of every one. We all can agree that it makes no economic sense to call the latest frontier model for simple tasks.
The leader of frontier capability changes often. We’re all aware that the top model of the day (and its vendor) changes every couple of months.
Your workflows may need custom models. Many teams are recognizing that intelligence is commodifying and the differentiator is custom models, derived from custom context.
As a result, all of us are quickly ending up with a portfolio of multiple models across multiple harnesses.
A similar convergence is happening one layer up. Developers pick certain tools for the right task, the way they always have. For example, perhaps Claude Code for long refactors, Codex for daily work, Hermes for quick scripts.
It’s reasonable to expect the future of work to be multi-model and multi-harness.
Which makes trust the defining question
A lot of agents work the same way.
They read material that is often out of our control: support tickets, web pages, documentation, and code written by strangers. But they act with authority you granted: your credentials, repo access, production APIs, and the open internet. And they usually do both from a developer’s laptop, outside typical security guardrails like VPCs and IAM.
Private data and the ability to act autonomously, together, is what makes an agent worth deploying. Your deputy needs the ability to execute in order to be useful. Which means the interesting question is no longer which model is best. It’s what happens when one of these deputies is wrong, or manipulated.
Per-harness guardrails break down
The obvious answer is that each harness ships its own guardrails. Many do. But relied on as your security boundary, they fail in three ways.
The agent talks past them. Guardrails inside the harness are enforced in the same loop the agent is running. Deny it a git push and it reaches for the API. Deny the API and it opens a gist. Deny the gist and it tucks the data into a channel you trust and never inspect. Researchers showed last year that a single malicious issue filed in a public GitHub repo could steer a coding agent into reading a company’s private repositories and publishing the contents in a pull request the agent opened itself. Nothing was hacked since every step used the agent’s own legitimate access, through a channel everyone trusts. A boundary the agent can negotiate with is not a boundary.
The rails move without you. Most harness’s isolation models are closed source and ship on their vendor’s schedule. The major coding agents have each revised their default sandbox and approval behavior several times in the past year alone. Updates to sandboxing models should be treated as a security event. Multiply this by ten harnesses and your security posture is, at any moment, whatever is the patchwork of your half dozen vendors’ measures.
The rails don’t cover the fleet. The custom agent your platform team built has exactly the guardrails your platform team wrote. The agent inside your support SaaS has whatever its vendor chose, and most expose no isolation controls to you at all. Every new harness means building or auditing governance again, from scratch, differently. You end up with a dozen implementations that drift apart, each blind to the others’ traffic, with no single place to set a rule and no single record to understand why something went wrong.
Safety cannot depend on the agent making the right decision, or on someone else’s release schedule.
A layer below
So here is what we believe. The future is multi-model and multi-agent. And given that future, we believe every organization will need a layer below: a runtime layer, below the harness, that all of them run on top of.
The reasoning is straightforward. Strip away the model, the vendor, and the framework, and an agent has two ways to affect anything. It runs code, which touches files and opens network connections. Or it calls a tool, which acts on a system. Everything an agent does travels one of those paths. And both paths cross the same surface: the runtime, where processes execute, credentials get used, and requests leave the machine. Every agent passes through it, no matter which model powers it, which vendor shipped it, or whether you built it yourself. That makes it the one place where rules you define can be enforced across all your agents. It is also the same fix as 1988, applied to today’s deputy: the authority sits a layer away.
Put enforcement there and each of the three failure scenarios we spoke about flips around.
Your agents can’t talk past themselves. The boundary for an agent sits outside the loop the agent is running, so it holds steady no matter if the model is with you, hallucinating, or compromised. A hard neutral boundary at the runtime is more effective than a prompt-level boundary the agent creates for itself.
The rails stop moving randomly. Policy is yours, written once, covering execution, tool calls, credentials, and spend. Now, a model or agent vendor making an update won’t randomly change your security posture.
The rails cover your whole fleet. A policy you write up will apply to every harness. And every action, by all your agents, lands in one record: what ran, what it touched, which rule decided.
This is what lets you be nuanced about agents. Without a boundary below your harnesses, you have three bad options: block agents completely, allow all of them and hope for the best, or wedge a manual approval into every step and give up the productivity you wanted.
A boundary at the runtime gives you a fourth option. When consequences are bounded even if an agent goes off the rails, you can start granting it true autonomy, which is the goal.
We expect models to keep changing and new harnesses to land in all of our toolkits. That part is healthy. The boundary underneath them is the part that should hold steady.
At We Are Developers in San Jose, Tushar Jain, Docker’s CTO, will talk more about this world: multiple models, multiple harnesses, and a single runtime under it all.
Everyone has been talking about the OpenAI/Hugging Face incident, and I was initially skeptical that Docker had much to add. After several weeks of customer conversations, I think we do. The useful lesson is not that an AI agent escaped a sandbox. It is what 17,600 actions expose about security systems designed for human tempo.
Hugging Face reconstructed approximately 17,600 attacker actions across a four-and-a-half-day campaign in July, including roughly two and a half days inside its infrastructure.
Put thirty seconds of human review against each action and you get 147 hours of work. Hugging Face grouped those actions into roughly 6,280 clusters; review only those and you still get more than 52 hours. Either way, manual approval and ordinary alert triage were never plausible primary controls for this workload.
This was not a fundamentally new category of attack. If you have any background in incident response, the sequence is familiar: exploit a reachable service, obtain code execution, collect credentials, escalate privilege, and cross the next trust boundary.
What was different was the rate, persistence, and ability to carry authority and state across short-lived environments. The agent could fail, rebuild its tooling, recover its communications channels, and continue from another environment without starting the campaign over. The useful mental model is a capable attacker fused with a fuzzer: it can reason about each result and keep probing without fatigue. That is the AI part of the story.
This is not a commentary on model alignment, and I will offer no opinion here on which model or agent framework you or your organization should use. The security question is familiar: what authority should be given to a workload that can execute code, hold credentials, reach the network, and continue testing paths long after a human would have stopped?
What happened
OpenAI was running internal cyber-capability evaluations against a benchmark called ExploitGym, which tasks agents with finding and exploiting vulnerabilities. The evaluation used reduced refusal behavior and none of the production-grade classifiers a deployed model would normally have.
The agents did not have direct internet access. They were allowed to install packages through an internally hosted package-registry cache. The agents found a previously unknown vulnerability in that permitted service and used it to reach the open internet.
The permitted service was the escape path. That’s the important point in the incident.
From there, the agents escalated privilege inside OpenAI’s research environment, reached a user-hosted CyberGym-style code-execution harness running on an external sandbox, and obtained root inside that harness. The sandbox platform itself was not compromised. Having apparently worked out that Hugging Face might host the benchmark’s reference answers, the agents went after Hugging Face directly, assessing that cheating was the fastest path to a passing score.
Hugging Face’s reconstruction documents local-file disclosure, code execution through a template engine, cloud metadata access, Kubernetes privilege escalation, exposure of a production secret containing 136 keys, access to the corporate network through a stolen credential, and limited write access to source control. It found no evidence that public models, datasets, packages, or released software were modified, and the customer content accessed was limited to a handful of benchmark-related datasets. That is a good outcome relative to what was possible. It is not evidence that the attack path was harmless.
The individual weaknesses were familiar: an unsafe processing path, accessible cloud metadata, broad privileges, long-lived credentials, and a shared administrative identity. A capable human attacker could have chained them, too. The difference was the volume of paths tested, the speed with which a failed path was replaced, and the amount of evidence defenders had to interpret while the campaign continued.
If you want the full blow-by-blow, Hugging Face published a detailed technical writeup and interactive replay. Both are worth reading.
The controls this requires
That arithmetic, not the novelty of the attack, is what changes the operating model. A human cannot sit in the control loop for thousands of actions at machine speed.
There are also two related threat models to account for.
In the first, the agent discovers and exploits a path on its own, as the agents did in this incident. In the second, an external party redirects the agent through a poisoned tool, compromised dependency, malicious document, prompt injection, or attacker-controlled data. In that case, the agent becomes the attack vector.
The same authority is dangerous in either case. The controls therefore have to govern what enters the environment, which capabilities the agent receives, and what happens when its behavior leaves the expected boundary.
What can go wrong
Control required
Untrusted content attempts to change the agent’s instructions
Treat external content as data, never authority; preserve provenance and trust boundaries; do not allow retrieved content to modify policy or grant capabilities
A tool, package, image, or MCP server is poisoned
Strict allowlisting; pin versions and digests; verify signatures and provenance; require SBOMs; scan and review updates before promotion
External data becomes executable input
Use structured and typed tool interfaces; validate and escape arguments; prohibit unsafe shell interpolation and other paths from data to command execution; run commands inside a sandbox and constrain which commands can be invoked
A permitted service becomes an escape path
Expose the narrowest operation-specific interface; scope access by task and destination; monitor permitted paths for behavior that diverges from their purpose
The workload reaches ambient credentials or control-plane services
Remove ambient credentials; use short-lived, task-scoped workload identity; block metadata and control-plane endpoints the task does not require
The workload exceeds its intended execution boundary
Use a hardened isolation boundary; reject privileged workloads, host filesystem mounts, raw device access, and unrestricted host sockets unless explicitly required
State or authority persists beyond replacement of the worker
Govern durable write paths; separate read from write authority; correlate identity and activity across runs
The agent, or an attacker acting through it, crosses into another system
Require explicit authorization for external and destructive actions; scope credentials to the target operation; make write paths observable and revocable
Thousands of individually plausible actions hide behavior that has diverged from the task
Evaluate sequences and bursts against policy and expected behavior; correlate activity across tools and systems; contain at machine speed
The point is not to put human approval in front of every action. That defeats much of the value of using agents and eventually produces approval fatigue. Routine and reversible actions should have very little friction. More sensitive, external, destructive, or unusual actions should receive stronger scrutiny.
Review and sanitization still matter, particularly for packages, tools, and paths where external data could become a command. But sanitization alone is not a reliable answer to prompt injection. Natural-language content is too ambiguous for us to assume we can always identify and remove the malicious part. The stronger boundary is architectural: untrusted content must not be able to grant itself authority, change policy, or create capabilities the agent did not already have.
Done well, governance is not what limits agent autonomy. It is what makes it possible to safely give agents more of it.
Where Docker fits today, and where we do not
We are proud to be founding authors of the Agent Baseline. We worked with other industry experts to distill the problem into six outcomes: Discover, Constrain, Authorize, Observe, Validate, and Respond.
If Docker Sandboxes sit in one specific bucket, it’s “Constrain,” but really, we believe they’re foundational, and where you would instrument or implement all six. They give each agent a dedicated microVM and enforceable boundaries around local compute, filesystem access, and network reach, as well as providing the base (and thus ground truth) layer to observe. That is a real and useful layer.
Docker AI Governance addresses parts of Authorize and Observe by giving organizations a centralized way to define and enforce controls around agent environments, including network and filesystem policies and access to MCP servers and tools.
Together, Sandboxes and AI Governance provide a meaningful part of the answer today: a hardened execution environment and centralized policy enforcement around it. They do not repair a vulnerable service the agent is authorized to contact, narrow a credential issued by another system, or replace the customer’s own security architecture. No vendor, Docker included, can claim its technology would have made this particular incident a non-event.
But a deterministic enforcement boundary is still necessary. It gives an organization one place to apply least capability and least privilege, and one place to observe what the agent was actually allowed to do. If an agent is using a package registry as an egress proxy rather than a package registry, that’s the kind of divergence the telemetry needs to help surface, especially when viewed across a sequence of requests rather than one request at a time.
The broader problem remains difficult. The useful unit of observation is not always one tool call. It may be a burst of activity, a target, a protocol, a credential, or a pattern visible only across systems. A package request can be normal. Repeatedly probing the service behind it, discovering credentials, and using them to reach another system should change the assessment.
That’s the agent-security challenge beyond basic containment. We need to constrain authority, but also observe activity at the right granularity, recognize when it deserves more scrutiny, and respond at the same tempo as the agent. For all of us, Docker included, there is still substantial work ahead across observation, validation, and response.
The operational tradeoff
Security, capability, and autonomy all matter, and they will always be in tension. Said differently, none of this is free.
Short-lived credentials expire during long-running tasks. Narrow egress policies break legitimate package installation. Admission controls reject tools developers assumed they could run. Cross-system detection costs money and produces false positives. A write approval inserted at the wrong point can eliminate most of the productivity the agent was supposed to provide.
Teams will be tempted to loosen each control until the agent works again. That is understandable. The failure mode created by a strict policy is immediate and visible; the failure mode created by excessive authority remains invisible until an incident.
The answer is not to remove the controls or ask a human to approve everything. It is to make friction proportional to consequence, test the failure modes, measure the operational cost, and weigh it against the risk and potential blast radius.
How I work
I use agents every day, and I assume that a sufficiently capable agent will eventually try something I did not anticipate (perhaps on a daily basis…).
For the most part, I do not run one general-purpose agent with access to everything. I use task-focused agents, each packaged as a separate kit, built on free Docker Hardened Images and run in Docker Sandboxes.
Each kit starts with a specific job, then receives only the software, network access, files, credentials, and external capabilities required for that job.
In most cases, the agent has very few restrictions inside its sandbox. That is intentional. What matters is that god mode inside the sandbox does not become god mode over my laptop, my credentials, or every service I can reach.
I do a lot of desk research. Those agents can access the open internet. They’re not useful if they can’t. But their image has no compilers, package manager, general-purpose network debugging tools, or development toolchain, and it runs with deliberately limited system permissions. They can retrieve and analyze public information, but have very little machinery with which to turn something they encounter into an exploit or act on another system. They have no reason to hold my source code or production credentials.
My production coding agent has a much richer environment. It runs pi, can use multiple models, compile code, run tests, and use the tools required for real engineering work. Its network access is restricted to an explicit allow list of services I use, including Docker, GitHub, Snowflake, and Cloudflare. It does not receive arbitrary internet access or arbitrary tools simply because a coding task occasionally needs the network.
My home kit can interact with an Arduino, but it does not receive direct access to the host or the device. A host-side MCP server brokers the allowed operations. The agent can request a defined Arduino capability through that interface; it cannot turn that permission into general access to every device connected to the machine.
My development kit is where I experiment. It runs with balanced network access, but no ambient host secrets and no unrestricted access to host files. When it needs Google Workspace, Snowflake, or another host service, host-side daemons broker those calls. The agent sees the capability I have chosen to expose, not the underlying credential or the rest of the service. Those brokers can enforce which operations are allowed and which are blocked.
These are deliberately different environments. The research agent would be poor at production coding. The coding agent cannot reach every site the research agent can. The home agent cannot turn an Arduino operation into arbitrary host access. The development agent can query a service without possessing the credential that authorizes the query.
That constraint is the feature.
Conclusion: Security at agent speed
The OpenAI/Hugging Face incident was not the failure of a single boundary. It was a chain of reasonable-seeming permissions and familiar weaknesses that became something very different when an agent could test thousands of paths, preserve state across runs, and carry authority from one system into the next.
We will not anticipate every vulnerability an agent might find or every way it might combine the access we give it. The architecture cannot depend on perfect agent behavior, perfect software, or a human noticing every dangerous action in time.
So, the starting point is still least capability and least privilege: give an agent the narrowest interface, credentials, tools, and network access its task requires. Put those controls at a deterministic enforcement boundary. Make the resulting activity observable, not only as isolated requests, but as sequences and patterns across systems. When the behavior leaves the expected envelope, containment has to happen at agent speed.
Docker Sandboxes and Docker AI Governance provide important parts of that architecture today: hardened execution boundaries and centrally enforced policy around them. They do not secure every service an agent is permitted to contact, and they do not eliminate the need for an organization to decide what authority each agent should have. The broader work across Discover, Constrain, Authorize, Observe, Validate, and Respond is why we helped create the Agent Baseline in the first place.
The goal is not to build an agent that never tries the wrong thing. The goal is to build a system where trying the wrong thing does not give it the keys to everything else.
Over the past few months, I’ve noticed something unmistakable in my conversations with customers. We’re no longer talking about what AI agents are capable of and whether they can transform the way we build software. We know the answer. They can. They already are.
The conversations I’m having now instead revolve around a much more sensitive, much more nuanced question: Can we trust these systems? Can we safely place them at the center of our business? That’s the question that’s already defining the next chapter of Agentic AI.
The world has been promised a paradigm-changing productivity boost from AI. For that to happen, we as technology leaders must empower customers with the solutions they need to build and maintain deterministic control over what agents can and can’t do. Developers and businesses alike need to have confidence that AI agents will behave predictably, operate within well-defined boundaries, and remain secure regardless of how quickly the underlying technology evolves.
Trust, not intelligence, will determine what’s truly possible in the agentic era. Intelligence comes from models. Trust comes from the runtime, identity, governance, and security surrounding them. That’s why we’re proud to join the Open Secure AI Alliance and why we’re grateful for NVIDIA’s leadership in bringing together organizations committed to solving this challenge. No single company can take on the task of building this trust alone. Security, safety, and governance have to be built through an open ecosystem that shares responsibility for moving the industry forward.
Speaking of open ecosystems, at Docker, we’ve always believed developers do their best work when they have the freedom to choose. That’s how we got to where we are today. It’s how we reshaped the container ecosystem and earned the trust of more than 20M developers worldwide. And it’s how we’re approaching the agentic era as well. We believe the true power of agentic AI can only be harnessed when customers can seamlessly route between open-weight and frontier models.
But this isn’t just what we believe; it’s what our customers are telling us they want. It’s what they’re telling us they need, today. Almost every customer I talk to has already made open-weight models a core part of their strategy. They need the ability to select the right model for the right task without having to rethink their architecture, rewrite their applications, or compromise on governance, safety, and security every time they make a different choice.
In other words, they need to be able to trust. Building that trust will require all of us. As AI agents become part of every software stack, trust has to extend beyond the model to the environments where agents execute. Docker is proud to help build that foundation alongside NVIDIA and the other members of the Open Secure AI Alliance.
The rapid rise of agentic AI in the enterprise is putting CISOs in a tough spot. On one hand, business leaders are clamoring to run with the new technology, which promises a productivity revolution like no other. On the other, setting AI agents loose without rigorous guardrails creates severe vulnerabilities.
Moriah put the dilemma facing CISOs like this: “The business wants AI agents everywhere, developers are already using them, sometimes without approval, oftentimes without security. …CISOs are left in this uncomfortable middle where we’re tolerating some tools, we’re praying that nothing breaks, we’re buying some time until we can get some governance beyond policy in place to have better visibility.”
The panel explored the role of the CISO in balancing this tension between productivity and security. Here are some highlights.
Isolate, control, observe
They came at it from different angles, but the panelists agreed on one imperative: running AI agents safely requires an isolated environment with trusted control boundaries. For Zach, Warp’s Oz platform provides that isolation. It’s a cloud agent infrastructure for secure and automated deployment of coding agents that allows centralized management, access controls, and visibility into what agents are doing across the organization.
Zach said you can “literally pull up the Oz web app and see what every agent across your company is doing at all times—which is a way better situation than the world we’re in right now, where someone on your marketing team is running Cloud Code, someone on your sales team is running Codex, and you just have no idea what’s going on, what tools they’re installing.”
NanoClaw—a personal AI agent
At Docker, our answer to the challenge of running AI agents safely is to run them in disposable, isolated, local sandboxes. Docker Sandboxes give agents the freedom and autonomy they need to do their best work, safely. Call it YOLO mode with guardrails. As Moriah noted in our discussion, agentic speed should be encouraged— “it’s the ungoverned speed that is the problem.” When agents are allowed to run fast without running wild, speed and safety are no longer a tradeoff.
Of note here: In March, we announced the integration of NanoClaw with Docker Sandboxes to deliver secure-by-design agent execution. The integration allows every NanoClaw agent to run inside a disposable, MicroVM-based Docker Sandbox that enforces strong operating system-level isolation. The stack takes advantage of NanoClaw’s minimal attack surface and fully auditable open-source codebase to meet enterprise security standards.
Laptops as the new prod
A key focus of the discussion was where to run agents safely. With vibe coding exploding, and agents and Claws (a new class of agents) already in production, the laptop today is the most powerful node in the enterprise. It’s also the most exposed. As a colleague of mine recently put it, laptop and agent environments are the new prod, and they need to be governed like prod.
Zach stressed the need to get agents off people’s laptops and desktops and into a controlled, cloud-based environment where CISOs can see what every agent across the company is doing at all times.
Portability—from laptop to cloud
My position is that, if you run agents in a sandbox, it doesn’t matter where the box sits. It could sit on a marketing or finance person’s laptop, or on a DevOps engineer or cloud admin’s machine. As long as the trust boundary is established and you know what’s getting piped in and out of it, you’re locked and loaded for rapid prototyping, experimentation, and innovation.
By the way, this view syncs with Docker’s vision, which has always been about portability. Our vision was never everything is local. We start in the local environment, then lift off into distributed environments, Kubernetes clusters, public clouds, whatever. It’s the same with agents. Eventually they’ll lift off, be decoupled from human operators, and be able to run fully autonomously wherever needed—always in the same portable environment.
When agents build the supply chain
The supply chain today is a revolving door for opportunistic attackers like TeamPCP and ShinyHunters who exploit transient dependencies and other vulnerabilities, often needing only a short window of time to filch credentials and information.
How are CISOs to combat these risks when AI agents themselves are pulling base images, choosing dependencies, and assembling code—autonomously and without human oversight? After all, in an autonomous supply chain, traditional methods of scanning and patching after building are no longer feasible.
Keeping humans in the loop
The panelists shared several best practices. Zach urged keeping humans in the loop for picking clean, secure dependencies, especially upstream libraries, and setting up blessed images for agents to choose from. Gavriel recommended setting a minimum release age of seven days for images and minimizing dependencies—even safe ones.
A training-wheels approach
Gavriel also suggested a training-wheels approach to experimenting with agents, starting out using unpermissioned data to build skills and avoid sensitive data issues. “Unlocking the value today is important,” he said, “but even more important is having people build the skills of working with agents, because what’s going to be coming in the coming months and years is going to totally exceed anything that we have today. So, it’s really about building the muscle memory, building the skills.”
Layer security to limit the blast radius
My take? Opportunistic attackers are simply exploiting an ecosystem that’s inherently flawed and broken and that unfortunately won’t get fixed within the next six to 12 months. Until then, developers should assume these attacks will continue and prepare for them by layering security to limit the blast radius. That means reducing privilege, reducing third-party access to their environment, and using immutable tags, digests, and SBOMs (Software Bill of Materials) to lock manifests and enable rapid detection of poisoned images. And, yes, outsourcing the risk to a trusted build environment like Docker that provides clean, hardened images so you’re starting from a clean foundation.
MCP—the new shadow IT?
The panelists rounded out the discussion with a focus on the security implications of using MCP in developer environments. MCP (Model Context Protocol) is a standard that allows LLMs to access external data and use tools, potentially making AI more powerful and reliable.
The consensus was that centralized, secure governance is crucial for productivity and risk management. Gavriel stressed the importance of proper version control, credential management, and a “golden repository” of verified tools. Zach advocated for centralized management to avoid individual tool dependencies and ensure minimal access.
Making it safe and easy to run MCP servers
And Docker? About a year ago, we introduced an open source MCP Gateway that serves as a chokepoint between agents and external tools. Routing every tool call through this enforcement point, where it can be authenticated, authorized, and logged before it reaches the external system, enables a wide range of agents to access trusted catalogs of MCP servers. While it’s not clear to me how long MCPs will remain useful, given the exponential speed with which AI is evolving, Docker MCP Gateway solves an important challenge today. Like Docker Sandboxes, it makes enforcement strict instead of advisory.
A once-in-a-generation opportunity—and challenge
Enabling dev environments to take advantage of agentic AI is a once-in-a-generation opportunity, and CISOs are accountable for making sure the rush to do so doesn’t devolve into the Wild West.
Moriah closed the panel with a provocative thought: “Six months from now, enterprises are all going to be running agents at scale. The one key success factor will be whether governance was present from day one or got bolted on after the first major incident.”
In the choose-your-own-adventure reality of agentic AI today, what kind of security leader will you be?
Why Docker is excited to co-host the first WeAreDevelopers World Congress North America
When we announced our partnership with WeAreDevelopers, AI agents were still mostly something developers experimented with. Today, they’re becoming part of everyday software development.
That’s why the timing for this year’s WeAreDevelopers World Congress couldn’t be better.
In the months since that announcement, the developer landscape has changed dramatically. If you’re writing software today, your workflow probably looks very different than it did a year ago. You’re prompting AI agents, reviewing AI-generated code, deciding what to accept and what to reject, and thinking about security much earlier in the development process.
Developers are no longer spending all of their time writing code. They’re designing systems that generate code, supervising autonomous agents, deciding what those agents can access, reviewing AI-generated changes, and making sure software is secure before it reaches production.
That shift feels a lot like the rise of data science a little over a decade ago. We didn’t replace programmers. We created an entirely new discipline that blended software engineering, mathematics, and statistics into something bigger.
I think we’re seeing the beginning of a similar transformation. Whether we continue calling ourselves developers, builders, or something entirely new almost doesn’t matter. The role itself is changing.
The best engineers of the next decade won’t simply write software. They’ll orchestrate teams of AI agents, establish the guardrails those agents operate within, and ultimately remain accountable for the systems they create.
That’s the conversation our industry needs to have. It’s also why this year’s WeAreDevelopers World Congress feels so important.
Docker is proud to serve as a presenting partner, but our goal isn’t to make this a Docker event.
Our goal is to help create a place where developers can learn from each other.
That’s why we partnered with WeAreDevelopers in the first place. They’ve spent more than a decade building one of the world’s strongest developer communities by focusing on the people building software, not the companies selling it. As AI reshapes how software gets built, North American developers need more than another vendor conference. They need a place to compare notes, share what’s actually working, challenge assumptions, and learn from peers facing many of the same questions.
The best developer conferences have never been about product launches. They’re about conversations. They’re about seeing how other engineers solve problems, discovering tools you didn’t know existed, and leaving with ideas you can actually use on Monday morning.
That’s what has made WeAreDevelopers so successful around the world, and that’s what we’re excited to help bring to the U.S.
The conversation has changed
Over the last year, nearly every conversation I’ve had with engineering leaders has landed in the same place.
Everyone wants the productivity gains that AI agents promise.
If you’ve spent any time with Claude Code, Cursor, Codex, or another coding agent, you’ve probably experienced it yourself. You can move faster than ever before. Then you stop and ask a different set of questions.
What is the agent actually doing?
Can it reach internal systems?
What credentials is it using?
Where is my data going?
How much autonomy am I comfortable giving it?
Those questions aren’t theoretical anymore. They’re becoming everyday engineering problems.
At Docker, they’ve shaped much of what we’ve been building.
We’ve introduced Docker Sandboxes so developers can run AI agents safely without changing how they work. We’ve launched Docker AI Governance to give organizations visibility and control over autonomous agents. We’ve continued investing in Docker Hardened Images because supply chain security only becomes more important as AI generates more code.
They’re all pieces of the same philosophy.
You shouldn’t have to choose between moving fast and staying secure.
The tooling should make both possible.
Meet the Docker team
We’ll have Docker engineers and leaders speaking throughout the event, including:
Mark Cavage, President & COO
Tushar Jain, EVP of Engineering & Product
Mark Lechner, CISO
We’ll also have engineers throughout the conference sharing what we’ve learned building for the next generation of software development, from AI-native workflows and developer productivity to security, containers, and the infrastructure that powers modern applications.
If you’ve been experimenting with agents, thinking about governance, or trying to figure out what secure AI development looks like inside your organization, we’d love to continue the conversation.
See you in San Jose
One thing has remained true throughout every shift in our industry.
Developers learn best from other developers.
That’s what makes communities like WeAreDevelopers special. It’s what has always made the Docker community special too.
AI will continue changing how software gets built. The tools will evolve. Our workflows will evolve right along with them.
What’s next won’t be shaped by any one company. It will be shaped by developers sharing ideas, challenging assumptions, experimenting with new ways of working, and building together.
That’s exactly what we hope to see in San Jose.
Whether you’re exploring AI agents for the first time, figuring out how to govern them at scale, or simply curious about where software engineering is headed next, we’d love to continue the conversation.
Come see what Docker is building for the next generation of software development, and join thousands of developers who are helping define what’s next.
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