Deploy AI agents safely at scale and with full control
Put AI agents into production without creating chaos
What software companies need before deploying AI agents
Stacklok’s Model Context Protocol (MCP) Platform sets clear boundaries, so you can responsibly use AI agents to ship better code, faster.
Fine-grained controls
Define exactly which APIs, services, and tools an AI agent can access and under what conditions. Prevent over-privileged agents from touching sensitive systems.
Integrated observability
Integrate with existing observability platforms (Datadog, OTel, Splunk) to monitor AI agent behavior in real time. Every tool call, action, and failure is logged and traceable.
Shared control plane
Provide a single, standardized interface for AI agent access across teams and products. Reduce duplication, eliminate one-off integrations, and give platform teams leverage.
Apply MCP to high-impact software platform use cases
Accelerate your existing AI initiatives and integrate with your current AI stack
Developer productivity
Embed internal data into developer workflows without over-privileging AI systems. Connect IDEs like Cursor or Claude Code to your identity provider (Okta, Entra, etc.) and observability solution.
Autonomous operations
Enable AI agents to assist with incident response, diagnostics, and routine operational tasks with strict guardrails around what actions agents are allowed to take.
Customer value-add
Power customer-facing MCP servers that create value, while leaning on recognized best practices and ensuring platform boundaries are always enforceable.
The State of MCP in Software 2026
We surveyed 100 technical leaders at category-leading software companies to explore their Model Context Protocol progress and priorities. Find out where you stand relative to your peers. Full study results are available with no forms to fill out.
Differentiated value for software and technology companies
Leaders trust our MCP platform because we:
Run in your VPC
Most MCP solutions are SaaS, which is a non-starter for financial services. 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 software companies that want to move fast without sacrificing security.
Stacklok’s Model Context Protocol Platform helps software and technology companies securely control how AI models access tools, data, and workflows. It provides a standardized, auditable way to define what context an AI system is allowed to use, under what conditions, and with what safeguards. This allows fast-moving software companies to deploy AI safely without exposing sensitive systems or data.
As AI agents become more autonomous, they increasingly interact with real systems like code repositories, databases, CI/CD pipelines, and cloud APIs. Model Context Protocol gives software teams a way to limit what AI systems can see and do by default. This reduces the risk of unexpected behavior while making AI-powered development more reliable and predictable.
Agent frameworks and prompt tools focus on how AI systems reason or plan. Stacklok focuses on what AI systems are allowed to access and execute once they are running. It acts as a control layer that sits alongside models and agent frameworks, ensuring safe interaction with real infrastructure.
By making AI permissions and context explicit, Stacklok allows developers to experiment and ship faster without manual review of every AI action. Guardrails are enforced by the platform rather than individual prompts or application logic. This reduces cognitive load on developers while keeping systems safe.
Yes. Stacklok is model-agnostic and designed to work alongside existing LLMs, IDEs, and internal systems. It provides a consistent control layer regardless of which models or vendors a firm uses, helping teams avoid lock-in while maintaining security. This is further reinforced by Stacklok’s work in the community and the company’s popular open source ToolHive MCP platform.