{"schemaVersion":1,"profileUrl":"https://nowen.ai/agents/stacklok-com","markdownUrl":"https://nowen.ai/agents/stacklok-com.md","jsonUrl":"https://nowen.ai/agents/stacklok-com.json","name":"Stacklok","legalName":"Stacklok, Inc.","website":"https://stacklok.com","location":"Seattle, Washington, USA","address":{"city":"Seattle","state":"Washington","country":"USA"},"industry":"AI Infrastructure and Enterprise Governance Software","deliveryModel":"software","status":{"trust":"unclaimed","text":"Unclaimed: compiled by Nowen from public sources, not reviewed by the company"},"updatedAt":"2026-10-30T03:55:43.571Z","summary":"Stacklok helps enterprises run and govern AI agents with an open architecture that supports security, flexibility, and control.","description":"Stacklok helps enterprises and developers run and govern advanced AI agents on their own infrastructure. Its open, self-hosted architecture supports secure access to tools and models, identity and policy controls, observability, and flexible deployment, enabling organizations to manage costs and data while avoiding vendor lock-in.","mission":"To ground AI in real-world systems.","directory":{"industryName":"AI Infrastructure and Enterprise Governance Software","agentCount":4,"indexable":false,"hubUrl":"https://nowen.ai/industry/ai-infrastructure-and-enterprise-governance-software"},"aiReaderFamilies":["Anthropic","Perplexity"],"products":[{"name":"Stacklok Enterprise MCP Platform","type":"Platform","url":"https://stacklok.com/platform/","summary":"Govern MCP servers and control AI access to enterprise tools and data while keeping deployments in your own environment.","description":"Stacklok Enterprise MCP Platform is a self-hosted platform for enterprises to curate, deploy, and govern Model Context Protocol (MCP) servers. Its registry, Kubernetes-native runtime, gateways, and portal help teams manage internal and remote servers, provide controlled access to tools and data, and integrate with existing identity and observability systems. The platform runs in a customer’s private cloud or Kubernetes environment and supports centralized authentication, authorization, auditing, and policy enforcement. Administrators can curate approved servers and skills, while developers and knowledge workers discover and use resources through supported clients and portal interfaces. Stacklok also describes an LLM gateway for securing agent-to-model actions and applying model-selection policies. The Enterprise MCP Platform is a hardened distribution of ToolHive, Stacklok’s open-source MCP platform; organizations can use the complete platform or selected components.","pricing":{},"features":[{"name":"Curated MCP registry","value":"Curate Trusted Servers And Skills","valueDescription":"Bring approved internal and external MCP servers and skills into one governed catalog. Administrators can review provenance, configure resources, and control who can discover and use them."},{"name":"Kubernetes deployment and lifecycle management","value":"Manage MCP Servers At Scale","valueDescription":"Deploy and operate MCP servers using Kubernetes infrastructure and management patterns. The operator and runtime help platform teams manage deployments and policies as the server estate grows."},{"name":"Isolated server runtime","value":"Contain Server Access And Risk","valueDescription":"Use container isolation and configurable network and filesystem permissions to restrict what each MCP server can access within the deployment environment."},{"name":"MCP gateway","value":"Centralize Governed Tool Access","valueDescription":"Give users and agents a controlled endpoint to approved MCP servers. Centralized gateway controls support consistent access management across internal and remote servers."},{"name":"Identity and access policies","value":"Enforce Identity-Based Access","valueDescription":"Apply authentication and authorization across MCP access using existing identity providers and policies. Per-user identity and token exchange help downstream systems attribute tool calls to the person who initiated them."},{"name":"Private-cloud deployment","value":"Keep MCP Workloads In Your Environment","valueDescription":"Deploy the platform within customer-controlled infrastructure to keep MCP workloads and data in the organization’s environment and support its deployment and data-residency requirements."},{"name":"Secure credential management","value":"Protect Server Credentials","valueDescription":"Store and use API tokens through encrypted storage or 1Password integration instead of placing credentials in plaintext configuration files."},{"name":"Audit and observability integrations","value":"Trace And Audit Tool Activity","valueDescription":"Send platform telemetry to existing observability systems and maintain centralized records of MCP activity, helping teams review usage and investigate actions."},{"name":"Enterprise portal and discovery","value":"Simplify Approved MCP Discovery","valueDescription":"Let administrators prepare approved resources centrally and give users a portal to discover and access available MCP servers, tools, and skills."},{"name":"Context and token optimization","value":"Reduce Irrelevant Tool Context","valueDescription":"Surface tools relevant to the task instead of sending the full tool manifest with every request. This can reduce token usage and limit distractions from irrelevant tool descriptions."},{"name":"Client compatibility and configuration","value":"Connect Supported AI Clients","valueDescription":"Connect supported development clients to governed MCP infrastructure, with automatic configuration for selected clients and manual setup available for other compatible clients."},{"name":"Server provenance verification","value":"Verify Server Origin Before Deployment","valueDescription":"Check MCP server provenance and detect tampering before deployment using integrations with Sigstore and GitHub Attestations."},{"name":"LLM gateway and model policies","value":"Control Model Access And Selection","valueDescription":"Apply policies to agent-to-model actions and define model-selection criteria to support control over model use and associated costs."},{"name":"Modular open-source foundation","value":"Adopt Components On Your Terms","valueDescription":"Use an end-to-end platform or select components to complement existing infrastructure, building on Stacklok’s ToolHive open-source foundation."},{"name":"Custom MCP server packaging","value":"Run Custom MCP Server Packages","valueDescription":"Run custom servers from container images or supported packages, with automatic container building available for several package types."}]},{"name":"Mecatl","type":"Platform","url":"https://mecatl.dev","summary":"Run and scale governed AI agents across local and Kubernetes environments while retaining flexibility to choose models, tools, and infrastructure.","description":"Mecatl is an open-source, cloud-native agent harness for running and managing agents, from local development to Kubernetes deployments and fleets. It separates the agent loop, tool calls, and untrusted execution into distinct layers, creating boundaries for isolation, policy enforcement, identity, and auditing. Its provider-independent loop can be assembled from a library of tools and skills, while the same backend supports local and Kubernetes operation.","pricing":{},"features":[{"name":"Agent fleet support","value":"Scale Agent Fleets","valueDescription":"Run agents on local infrastructure or Kubernetes, with an architecture designed to support deployments from individual developers through platform teams."},{"name":"Separation of agent, tools, and execution","value":"Isolate Untrusted Execution","valueDescription":"Keep untrusted execution separate from the agent loop and its credentials, creating explicit boundaries for security and platform controls."},{"name":"Identity, policy, and audit controls","value":"Govern And Audit Agent Actions","valueDescription":"Apply identity and policy controls at the harness boundaries and create audit records through the components that enforce permissions."},{"name":"Provider-independent agent loop","value":"Choose Models And Infrastructure","valueDescription":"Keep the agent loop independent of model providers and infrastructure so teams can change models or vendors without replacing the harness."},{"name":"Local and Kubernetes portability","value":"Promote Workflows Across Environments","valueDescription":"Use the same backend locally and on Kubernetes to move workflows from development toward production without changing harness configuration."},{"name":"Composable harness","value":"Assemble A Tailored Harness","valueDescription":"Build a harness from a library of tools and skills, with tools and MCP services available through an explicit catalog."},{"name":"Headless operation and multiple clients","value":"Connect Multiple Clients","valueDescription":"Let different clients attach to the same agent loop, including terminal, API, Slack, and web interfaces."},{"name":"Durable session state and event history","value":"Resume Sessions After Worker Loss","valueDescription":"Persist session state and event history so a replacement worker can resume from the last saved turn if a worker stops."},{"name":"Open-source and self-hosted deployment","value":"Run On Your Infrastructure","valueDescription":"Deploy Mecatl locally or on Kubernetes without relying on Stacklok-managed infrastructure."}]},{"name":"Broodbox","type":"Product","url":"https://github.com/stacklok/brood-box","summary":"Run coding agents in isolated microVMs and review their changes before they reach your workspace.","description":"Broodbox is an open source project for running coding agents in hardware-isolated microVMs and reviewing every change before it reaches your workspace.","pricing":{},"features":[{"name":"Hardware-isolated agent execution","value":"Isolate Agent Execution","valueDescription":"Run coding agents in hardware-isolated microVMs to keep their execution separate from your workspace."},{"name":"Change review","value":"Review Changes Before Applying","valueDescription":"Inspect every change made by an agent before it reaches your workspace."}]},{"name":"MCP deployment and enablement services","type":"Service","url":"https://stacklok.com/solutions/deployment/","summary":"Helps organizations move MCP from planning to production with registry guidance, implementation, workflow support, and ongoing operations.","description":"Stacklok helps organizations move MCP from idea to a production-ready registry. Services include advisory support for registry curation strategy and compliance alignment, implementation support for registry setup and policy enforcement automation, and managed services for ongoing maintenance, updates, and monitoring. Stacklok also offers Forward Deployed Engineers who embed with teams to build critical workflows and accelerate adoption.","pricing":{},"features":[{"name":"Implementation support","value":"Set Up Registry And Policy Automation","valueDescription":"Get help setting up an MCP registry and automating policy enforcement as you move toward production."},{"name":"Advisory services","value":"Plan A Governed MCP Registry","valueDescription":"Get advisory support for registry curation strategy and compliance alignment as you plan your MCP deployment."},{"name":"Forward deployed engineering","value":"Build Critical Workflows With Embedded Engineers","valueDescription":"Work with Stacklok engineers embedded with your team to build critical workflows and accelerate MCP adoption."},{"name":"Managed services","value":"Maintain And Monitor MCP Deployments","valueDescription":"Receive ongoing maintenance, updates, and monitoring to support continued MCP operations."},{"name":"Client integration","value":"Connect MCP Servers To Client Tools","valueDescription":"Use built-in, one-click integration with clients such as Cursor and Claude to simplify access to MCP servers."}]}],"audiences":[],"segments":[{"name":"MCP server management","description":"Centralized curation, deployment, discovery, and lifecycle management of Model Context Protocol servers, giving enterprise teams controlled access to approved tools and data integrations.","size":0.2,"cagr":40,"color":1,"productNames":["Stacklok Enterprise MCP Platform"],"methodology":"The search results contain no explicit revenue estimate or CAGR for MCP server management. The $0.2B estimate is a directional estimate for the current global enterprise curation, deployment, and governance layer, not the broader MCP server or AI software market. The 40% CAGR reflects the category’s early stage and rapid ecosystem expansion; it is an estimate, not a source-reported market forecast.","citations":[{"url":"https://www.naxiaglobal.com/blog/mcp-model-context-protocol-business-en/","text":"more than 10,000 active public MCP servers and more than 97 million monthly SDK downloads"},{"url":"https://neuroflash.com/blog/mcp-api/best-mcp-servers-market-research/","text":"Anthropic reported over 10,000 active public servers and 97 million monthly SDK downloads by the end of 2025"}]},{"name":"AI agent security and governance","description":"Controls that secure agent execution and access to tools, data, and models through workload isolation, identity enforcement, access policies, provenance verification, and auditing.","size":0.3,"cagr":40,"color":2,"productNames":["Stacklok Enterprise MCP Platform","Broodbox"],"methodology":"The search result estimates the broader AI agent market at $8 billion in 2025, growing to $199 billion by 2034 (about 43% CAGR). Since it provides no direct figure for security and governance, the $0.3 billion segment size and 40% CAGR are estimates: the segment is treated as a small, rapidly growing share of the broader market.","citations":[{"url":"https://www.brookings.edu/articles/shaping-global-governance-of-ai-agents/","text":"the market will grow from $8 billion in 2025 to $199 billion by 2034"}]},{"name":"AI agent orchestration","description":"Orchestration and composition of domain-specific AI agents, connectors, and automated workflows to create agentic experiences across enterprise functions.","size":1.4,"cagr":40,"color":3,"productNames":["Mecatl"],"methodology":"The search results contain no relevant market-size or growth data. Estimated from internal knowledge: AI agent orchestration is a fast-growing subset of enterprise AI software, smaller than the broader AI agent market. No search-result citations explicitly support these estimates.","citations":[]},{"name":"AI implementation and managed services","description":"Advisory, implementation, embedded engineering, and ongoing operational support for enterprises deploying MCP registries, integrations, and governed agent workflows.","size":10,"cagr":30,"color":4,"productNames":["MCP deployment and enablement services"],"methodology":"No search results were provided. These are directional global estimates for enterprise AI implementation and ongoing managed services, inferred from the broader AI services market; the specified MCP and governed-agent workflow offerings are a narrower, emerging subset.","citations":[]}],"faq":[],"comparisons":[],"sources":[{"url":"https://stacklok.com","text":"Stacklok"},{"url":"https://stacklok.com/platform/","text":"Stacklok Enterprise MCP Platform"},{"url":"https://mecatl.dev","text":"Mecatl"},{"url":"https://github.com/stacklok/brood-box","text":"Broodbox"},{"url":"https://stacklok.com/solutions/deployment/","text":"MCP deployment and enablement services"},{"url":"https://www.naxiaglobal.com/blog/mcp-model-context-protocol-business-en/","text":"more than 10,000 active public MCP servers and more than 97 million monthly SDK downloads"},{"url":"https://neuroflash.com/blog/mcp-api/best-mcp-servers-market-research/","text":"Anthropic reported over 10,000 active public servers and 97 million monthly SDK downloads by the end of 2025"},{"url":"https://www.brookings.edu/articles/shaping-global-governance-of-ai-agents/","text":"the market will grow from $8 billion in 2025 to $199 billion by 2034"}]}