What Do You Use for AI Agent Infrastructure? The Complete Guide to Building Production-Ready Agent Systems

The question "what do you use for AI agent infrastructure?" has become one of the most searched queries in the DevOps and platform engineering space. And for good reason: the global AI agent market is projected to grow from $5.1 billion in 2024 to $47.1 billion by 2030, representing a compound annual growth rate of nearly 45%. With 85% of enterprises expected to implement AI agents by the end of 2025, getting the infrastructure right has never been more critical.

But here's the challenge most teams face: AI agents aren't like traditional applications. They're non-deterministic, they execute code dynamically, they interact with external systems in unpredictable ways, and they require isolation that goes far beyond what conventional container orchestration provides. If you're wondering what infrastructure stack can actually handle these requirements in production, you're not alone.

When teams first start building AI agents, they often try to run them on existing infrastructure—a Kubernetes cluster here, some Docker containers there. This approach quickly reveals its limitations. AI agents generate and execute code at runtime, meaning you can't predict what system calls they'll make or what resources they'll need. A traditional container running an AI agent could attempt to access the filesystem, make network requests to arbitrary endpoints, or consume memory in ways that affect other workloads.

The non-deterministic nature of large language models compounds this problem. Unlike a REST API that returns predictable responses, an AI agent might decide to write a script that deletes files, opens network connections, or interacts with databases in unexpected ways. Running such workloads without proper isolation is like giving an untrained intern root access to your production servers—even with the best intentions, things can go wrong quickly.

This is precisely why the concept of sandboxing has become central to AI agent infrastructure. A sandbox provides an isolated environment where untrusted code can run without affecting the host system or other workloads. For AI agents, sandboxing isn't optional—it's foundational.

Building production-ready infrastructure for AI agents requires thinking about several interconnected layers. Let's break down what each layer needs to accomplish and what options exist.

The execution layer is where your agents actually run code and perform actions. This layer needs to provide strong isolation while maintaining enough performance for interactive use cases. Several approaches have emerged, each with different tradeoffs.

Virtual machines offer the strongest isolation but come with significant overhead. Spinning up a full VM for each agent execution can take seconds to minutes, and the resource requirements make them impractical for high-volume scenarios. Container-based isolation provides better performance but weaker security boundaries—a sophisticated attack might escape a container and access the host system.

More recently, specialized sandboxing technologies have gained traction. Google's gVisor intercepts system calls in user space, providing VM-like isolation with container-like performance. WebAssembly (WASM) sandboxes offer another approach, particularly useful for running code in browser environments where traditional isolation mechanisms aren't available.

For teams building production AI agent systems, platforms like HopX provide purpose-built sandboxing infrastructure specifically designed for AI agents. Rather than cobbling together VMs, containers, and custom security configurations, HopX offers turnkey sandbox environments that spin up in milliseconds and provide the isolation guarantees that agent workloads require. This kind of specialized infrastructure dramatically reduces the engineering effort needed to safely deploy agents at scale.

Once you can safely execute individual agent tasks, you need to orchestrate environments at scale. This means automatically provisioning sandboxes when agents need them, tearing them down when work is complete, and managing the lifecycle of potentially thousands of concurrent execution environments.

Kubernetes has become the de facto standard for container orchestration, and Google recently introduced Agent Sandbox as a new Kubernetes primitive specifically designed for agent workloads. Agent Sandbox provides kernel-level isolation through technologies like gVisor and Kata Containers, along with features like pre-warmed pools for sub-second latency and pod snapshots for fast environment restoration.

However, Kubernetes alone doesn't solve the orchestration challenge. You still need tooling to define what environments contain, how they should be configured, and how they interact with your application code. This is where environment-as-a-service platforms become valuable.

AI agents don't exist in isolation—they're part of larger applications that need to be developed, tested, and deployed like any other software. This is where the concept of ephemeral environments becomes essential.

An ephemeral environment is a short-lived, isolated deployment of an application that's automatically created for testing and destroyed when no longer needed. For teams building AI-powered applications, ephemeral environments serve multiple purposes. They provide isolated spaces to test agent behavior without affecting production systems. They enable parallel development where multiple team members can work on different features simultaneously. And they support the kind of iterative experimentation that AI development requires.

Bunnyshell pioneered this approach with its Environment-as-a-Service platform, which automatically spins up complete preview environments for every pull request. When you're developing AI agent features, having a full-stack environment that replicates production—complete with databases, services, and integrations—lets you test agent behavior in realistic conditions before merging code.

The combination of agent-specific sandboxing (like HopX provides) with full-stack preview environments creates a powerful development workflow. Agents execute safely within their sandboxes, while the broader application context ensures you're testing realistic scenarios.

Not all AI agents have the same infrastructure requirements. The right approach depends on what your agents are doing and what constraints you're operating under.

Coding assistants like Cursor, GitHub Copilot, and similar tools represent one of the most common agent patterns. These agents generate code that needs to be executed to verify correctness, run tests, or produce outputs. The infrastructure challenge is executing arbitrary, LLM-generated code safely and efficiently.

For these use cases, low-latency sandboxing is essential. Developers expect near-instant feedback when they ask an agent to run code or execute tests. Platforms optimized for code execution typically offer pre-warmed environments, persistent file systems within sessions, and support for multiple programming languages and their respective toolchains.

The development workflow also matters. When agents generate code that will eventually be deployed, you need environments where that code can be tested in context. Preview environments that automatically deploy with each code change enable testing agent-generated code in realistic conditions before it reaches production.

More sophisticated agents operate autonomously, making decisions and taking actions without constant human oversight. These agents might browse the web, interact with APIs, manage files, or coordinate with other systems to accomplish complex goals.

The infrastructure requirements here are more demanding. Autonomous agents need access to tools and external systems, but that access must be carefully controlled. Network isolation prevents agents from accessing unauthorized endpoints. Resource limits ensure a runaway agent can't consume infinite compute. Audit trails record every action for later review.

HopX addresses these requirements by providing configurable sandboxes with fine-grained control over what agents can access. You can specify exactly which network endpoints are allowed, what filesystem paths are accessible, and what resources are available—creating a security perimeter that matches your risk tolerance.

The frontier of agent development involves systems where multiple agents collaborate, with orchestrator agents delegating tasks to specialized worker agents. Frameworks like AutoGen, CrewAI, and LangGraph have emerged to support these patterns.

Multi-agent systems compound infrastructure challenges. Each agent needs isolation from others to prevent unintended interactions. Communication between agents must be explicit and controlled. And the orchestration layer needs visibility into what every agent is doing.

For teams building multi-agent systems, the combination of individual agent sandboxing with environment-level orchestration becomes critical. Each agent runs in its isolated sandbox, while the broader environment provides the coordination substrate and shared context that enables collaboration.

Given the complexity involved, how should teams approach building their agent infrastructure? Here's a practical framework.

Start with isolation requirements. Determine what level of isolation your agents need based on what they're doing. Agents that only read data have different requirements than agents that execute arbitrary code or make network requests.

Choose your sandboxing approach. For most production use cases, purpose-built agent sandboxing platforms like HopX provide the fastest path to secure agent execution. Building sandboxing infrastructure from scratch requires deep expertise in container security, system call interception, and resource isolation—expertise most teams don't have in-house.

Integrate with your development workflow. Agent code still needs to be developed, tested, and deployed like any other software. Preview environments through platforms like Bunnyshell ensure you can test agent behavior in realistic conditions before reaching production.

Plan for observability. You can't manage what you can't measure. Ensure your infrastructure provides logging, tracing, and monitoring for agent executions. Understanding what agents are doing—and what resources they're consuming—is essential for both debugging and cost management.

Consider the full application context. AI agents are typically components within larger applications. Your infrastructure needs to support the entire stack, not just the agent execution layer. This is where environment-as-a-service approaches shine, providing complete application environments that include your agent infrastructure alongside databases, APIs, and frontend services.

Looking at how leading engineering teams are implementing AI agent infrastructure provides valuable insights. Companies ranging from startups to Fortune 500 enterprises are converging on similar patterns, even if the specific tools differ.

The most successful implementations share a common thread: they treat agent infrastructure as a first-class concern rather than an afterthought. Teams that bolted agent execution onto existing infrastructure consistently report higher incident rates, more security concerns, and greater operational overhead than teams that designed for agents from the start.

A typical production setup includes three layers. The execution layer handles individual agent tasks in isolated sandboxes—this is where HopX fits, providing the secure runtime environment that agents need. The orchestration layer manages the lifecycle of these sandboxes, handling provisioning, scaling, and cleanup. The application layer integrates agent capabilities into the broader product, connecting sandboxed execution to databases, APIs, and user interfaces.

For development and testing, teams increasingly rely on ephemeral preview environments that replicate this entire stack. When a developer pushes code that modifies agent behavior, Bunnyshell automatically provisions a complete environment—including the agent sandbox layer—where the changes can be validated before merging. This "shift left" approach catches issues early, before they can impact production users.

The cost economics of this approach are compelling. Running persistent infrastructure for agent workloads—infrastructure that sits idle most of the time—is expensive. Ephemeral approaches that spin up resources only when needed can reduce infrastructure costs by 40-60% while improving developer productivity through faster feedback loops.

We're still in the early days of AI agent infrastructure. The patterns and tools are evolving rapidly as teams learn what works in production. Several trends are worth watching.

Standardization is emerging around primitives like Kubernetes Agent Sandbox, which provides a common foundation for agent execution across cloud providers. This standardization will make it easier to build portable agent infrastructure that works across different environments.

The line between development and production is blurring. When agents can modify code, run tests, and even deploy changes, the traditional distinction between "development environment" and "production environment" becomes less meaningful. Infrastructure needs to support continuous experimentation while maintaining production stability.

Security will become increasingly sophisticated. As agents become more capable, the attack surface expands. Expect to see more innovation in areas like anomaly detection for agent behavior, fine-grained permission systems, and automated threat analysis for agent actions.

The question of what to use for AI agent infrastructure doesn't have a simple answer. The right approach depends on your specific use case, security requirements, and existing technology stack. But the fundamental principles are clear: you need strong isolation for agent execution, orchestration for managing environments at scale, and integration with your development workflow for testing and iteration.

Platforms like HopX for agent sandboxing and Bunnyshell for preview environments represent the emerging stack that production teams are adopting. Rather than building complex infrastructure from scratch, these purpose-built platforms let you focus on what matters—building agents that deliver value for your users.

The teams that get their agent infrastructure right will have a significant advantage as AI agents move from experiments to production workloads. The time to start building that foundation is now.