Manufacturing firms trust Stacklok’s AI agent guardrails
Put AI agents into production without compromising security, compliance, or operational continuity
Solve manufacturing-specific challenges to optimizing use of AI agents
Stacklok’s Model Context Protocol (MCP) Platform is built for complex environments, so you can move from experimentation to production.
Security by design
Enforce least privilege for AI agents with authorization, network isolation, and secure token exchange. AI agents only interact with the systems and data you explicitly allow.
Compliance and auditability
Integrate with your existing observability stack to generate a complete record of AI tool usage. Every action is traceable, reviewable, and defensible.
Operational simplicity
Provide engineers, developers, and knowledge workers with a single, controlled endpoint to access the exact tool(s) an AI agent is permitted to use.
Apply MCP to high-impact manufacturing use cases
Accelerate your existing AI initiatives and integrate with your current AI stack
AI agent guardrails
Manufacturing firms expanding use of AI agents from developers to mechanical engineers and knowledge workers value Stacklok’s ability to permission and pre-configure MCP servers.
Enterprise system integration
Enable AI agents to query and act across ERP, CRM, and supply chain platforms while maintaining full per-user identity passthrough. Every downstream system logs the actual end user, not a service principal.
Access and accountability
Layer natural language interfaces and AI-driven insights on top of traditional ML and manufacturing workloads, without exposing sensitive production data or over-privileging automated systems.
Why manufacturing leaders choose Stacklok
Leaders trust our MCP platform because we:
Run in your VPC
Most MCP solutions are SaaS, which is a non-starter for manufacturing environments with strict data governance and air-gap requirements. Stacklok runs in your Virtual Private Cloud, so your data stays in your environment.
Built on open source
Stacklok builds in the open, with the community. Our popular ToolHive platform can solve many of your MCP challenges immediately, and ensures a sustainable path forward
Offer a full platform
Start by curating your own MCP registry or implementing a custom MCP gateway, and then expand to the complete Stacklok MCP platform according to your timeline and need
Take the next steps
Continue with your due diligence and know that we’re always available for an open conversation
for Enterprise
Start by curating a registry of trusted MCP servers for your enterprise
for Individuals
Dive into the ToolHive repo and docs, and then engage directly with our team.
Frequently asked questions
Stacklok’s Enterprise Model Context Protocol Platform is designed for manufacturing firms that operate with rigor in regulated environments.
Manufacturing organizations operate with interconnected systems — ERP, MES, data platforms, and engineering tools — where uncontrolled AI access can create serious operational and compliance risk. Model Context Protocol enables manufacturers to constrain AI behavior by design, limiting which systems an AI can interact with and how. This reduces risk while still allowing teams to accelerate productivity and insight across the enterprise.
Manufacturing organizations typically begin their MCP journey with software and platform engineering teams, who are comfortable with container-based tooling and IDE integrations. Expanding to mechanical engineers, hardware teams, and other knowledge workers requires a fundamentally different experience — one that doesn’t assume familiarity with command-line interfaces or local configuration.
Stacklok supports this expansion in two ways. First, the platform’s hosted deployment model means non-developer users never need to install or configure anything locally. MCP servers are centrally managed and accessed through a clean, self-service portal rather than a developer CLI. Second, administrators can define role-based access policies that surface only the tools and data sources relevant to a given team or function — so a mechanical engineer sees a curated, purpose-built experience rather than the full tool catalog available to a platform engineer.
A common challenge in manufacturing AI deployments is that downstream systems end up logging a shared service account rather than the actual end user making a tool call. This breaks audit trails and creates compliance exposure — particularly in environments where access to ERP or supply chain data is tightly regulated.
Stacklok solves this through per-user OAuth and token exchange. When a developer or knowledge worker invokes an MCP tool, their identity flows through the platform to the downstream system, so Databricks, SAP, or Salesforce logs the real user — not a service principal. This preserves end-to-end accountability without requiring each downstream system to implement its own authentication logic.
In most manufacturing organizations, the bottleneck to scaling MCP isn’t technical — it’s the security review. AppSec and GRC teams need clear, auditable answers about where MCP servers live, what data they can access, and how authentication and authorization are enforced before they’ll approve production deployment.
Stacklok is designed to give your security team exactly what they need to say yes. The platform enforces least-privilege access policies, generates structured audit logs that flow into existing SIEM platforms, runs entirely within your own infrastructure, and supports the policy frameworks — including Cedar, OPA, and Rego — your teams may already use. Rather than asking security to trust a black box, you can walk into a review with a concrete, defensible architecture.