Anthropic has introduced two significant enhancements to its Claude Managed Agents offering: self‑hosted sandboxes and Model Context Protocol (MCP) tunnels. These additions aim to give enterprises greater control over where their AI workloads run, how data moves between agents and external systems, and how they meet stringent security and compliance requirements. The changes reflect a broader trend toward hybrid AI deployments that combine the power of cloud‑based models with the privacy guarantees of on‑premise infrastructure.
Self‑hosted sandboxes allow organizations to execute Claude agents within environments they fully manage. Rather than relying solely on Anthropic’s hosted execution environment, customers can deploy the sandbox runtime inside their own virtual private clouds, dedicated hardware, or air‑gapped networks. This capability isolates agent execution from the public internet, reducing the attack surface and ensuring that any code or data processed by the agent remains inside the organization’s trust boundary. The sandbox provides a consistent runtime interface that mirrors the cloud version, so existing agent definitions, prompts, and tool integrations continue to work without modification. Administrators can apply their own hardening measures, such as custom network policies, intrusion detection systems, or hardware security modules, to further protect the workload.
MCP tunnels complement the sandbox by establishing a secure, low‑latency channel between a Claude agent and external data sources or services. The Model Context Protocol is a lightweight, message‑oriented framing layer designed specifically for AI agents that need to query databases, call APIs, or retrieve files while preserving context across interactions. When an agent runs inside a self‑hosted sandbox, MCP tunnels enable it to reach back‑end systems that may reside in the same private network or in a partner’s environment without exposing traffic to the public internet. The tunnels encrypt all payloads using mutual TLS, authenticate both ends with short‑lived certificates, and support multiplexing of multiple logical streams over a single connection. This design minimizes overhead and avoids the complexity of traditional VPNs or custom gateway solutions.
Together, these features address several common enterprise concerns. First, data residency requirements are easier to satisfy because agents can process sensitive information never leaving the organization’s controlled network. Second, latency-sensitive applications benefit from keeping compute and data close together, reducing round‑trip times that would otherwise be incurred by routing requests through a public cloud. Third, the ability to audit and control the execution environment simplifies compliance with standards such as SOC 2, ISO 27001, or HIPAA, as organizations can apply their existing security frameworks directly to the AI workload.
From a technical perspective, the sandbox runtime is packaged as a container image that includes the Claude inference engine, a minimal OS base, and the MCP client library. Anthropic provides Helm charts and Terraform modules to streamline deployment on Kubernetes clusters or virtual machines. The MCP tunnel component runs as a sidecar proxy alongside the agent, handling connection establishment, encryption, and reconnection logic. Configuration is handled through a declarative manifest where operators specify allowed endpoints, authentication methods, and bandwidth limits. Anthropic’s documentation emphasizes that the sandbox does not modify the model weights; it merely provides a secure execution envelope, preserving the same performance characteristics as the managed service.
The introduction of these capabilities also influences the pricing model. While the core Claude Managed Agents service remains available as a fully managed offering, customers who opt for self‑hosted sandboxes pay for the underlying infrastructure they consume, plus a nominal fee for the sandbox runtime license. MCP tunnels are included at no extra charge when used with a sandbox deployment, encouraging organizations to adopt the private‑network pattern without incurring additional per‑tunnel costs.
Early adopters have reported improvements in scenarios such as internal knowledge‑base querying, where agents need to access proprietary document stores that cannot be exposed to the internet, and real‑time analytics pipelines that require sub‑second responses from agents interacting with high‑frequency trading systems. By keeping the agent and its data sources within the same trust domain, these use cases achieve both stronger security guarantees and predictable performance.
Anthropic positions the sandbox and MCP tunnel features as part of a longer‑term strategy to make its AI models adaptable to diverse deployment contexts. The company notes that future work will focus on extending the sandbox with additional isolation technologies, such as confidential computing enclaves, and enriching MCP with support for streaming data formats and standardized schema definitions. These roadmap items suggest a commitment to evolving the managed agents platform alongside emerging privacy‑enhancing technologies and industry standards.
In summary, the addition of self‑hosted sandboxes and MCP tunnels to Claude Managed Agents gives enterprises the flexibility to run AI workloads where they need them, while maintaining the security, latency, and operational controls expected of critical infrastructure. By decoupling agent execution from the public cloud and providing a purpose‑built tunneling mechanism for secure data access, Anthropic addresses a growing demand for AI solutions that can operate safely within regulated or highly private environments. The features are generally available now, with detailed deployment guides and reference architectures accessible through Anthropic’s developer portal.
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