For the complete documentation index, see llms.txt. Markdown versions of all docs pages are available by appending .md to any docs URL.

Learn more about Solo Enterprise for agentregistry.

Solo Enterprise for agentregistry is an enterprise-grade AI artifact platform to manage the lifecycle of your AI artifacts, including AI agents, MCP tool servers, skills, and more. With Solo Enterprise for agentregistry, you can easily orchestrate deployments across providers, and monitor the efficiency and cost of your AI deployments. The product builds on the open source agentregistry project.

Challenges with AI Artifacts

As organizations build AI applications, they face significant challenges in discovering, managing, and governing AI artifacts across different environments and teams. These challenges apply broadly to all types of AI artifacts—agents, MCP servers, skills, and other AI components—regardless of the protocols or frameworks they use.

Tribal knowledge and informal sharing

Without a registry, teams fall back on informal channels: MCP server endpoints shared in Slack, skill files passed over email, and word-of-mouth about which prompts work best. This tribal knowledge breaks down as teams grow:

• Developers can’t tell which artifacts exist, which are approved, or which are still actively maintained.
• A developer builds a useful skill and shares it with a colleague. When they iterate and improve it, the colleague is still running the old version, with no way to know that a newer version exists.
• MCP servers get stood up ad-hoc and shared informally. Over time, no one knows which are vetted by security, which are still running, or which expose sensitive capabilities.

This kind of fragmentation leads to duplicated work, inconsistent implementations, and security blind spots.

Discovery across environments

One of the most pressing challenges is discovering which AI artifacts are available and where they can be found. AI artifacts are often scattered across multiple container registries, GitHub repositories, internal development environments, third-party sources, and cloud environments. Without a centralized catalog, developers struggle to find what AI artifacts already exist and are approved to use. Teams also lack the metadata and search capabilities to filter artifacts by use case, version, or status, turning discovery into a time-consuming manual process.

Skills and prompt version drift

Skills and prompts evolve constantly. A developer iterates on a skill ten times, but everyone they originally shared it with is still on version one. There is no way to notify consumers that a newer version exists, no single place to publish the latest iteration, and no mechanism to ensure that agents built by different teams are using the same approved prompt baseline.

Multi-cloud agent sprawl

Agents are deployed to different environments and platforms, such as Kubernetes, GCP Vertex, and AWS AgentCore. Each platform has its own deployment model, observability stack, and access controls. Applying the same policies to all of these environments can be challenging and time consuming. In addition, the lack of a unified AI artifact lifecycle makes it hard for developers to build, version, discover, govern, and deploy agents to multiple clouds.

Governance and approval

Organizations need to ensure that only approved, secure, and validated AI artifacts are used in production environments. However, the current landscape lacks mechanisms for:

• Approval workflows and cross-environment governance: There’s no standardized way to review, approve, and control which artifacts are available to teams. With artifacts being spread across multiple environments, repositories, and registries, it can become challenging to enforce the same security policies, quality standards, and compliance requirements.

• Visibility into artifact status: Teams often don’t know which artifacts are approved for use. This lack of visibility can lead to security risks and compliance violations.

• Audit and compliance: Maintaining audit trails that show who approved an artifact and when becomes crucial to meet legal and compliance requirements.

Artifact registry infrastructure

The infrastructure for managing AI artifacts at scale is fragmented and incomplete:

• No standardized storage: There’s no unified way to store, version, and distribute AI artifacts across organizations and teams. Artifacts end up in different container registries, code repositories, or internal systems.

• Naming and resolution: Without a centralized naming and resolution system, it’s unclear how to resolve artifact names to their actual locations, versions, or deployment endpoints across different environments.

• Lifecycle management: Managing the complete lifecycle of artifacts—from creation through deployment, updates, and deprecation—requires bundling together multiple tools and processes.

• Metadata enrichment: While some artifacts have basic metadata, there’s no systematic way to enrich artifacts with validation scores, security assessments, usage analytics, or other contextual information that helps teams make informed decisions.

These challenges make it difficult for organizations to build production-ready AI applications where teams can confidently discover, deploy, and manage AI artifacts at scale. Without proper discovery and governance mechanisms, organizations risk security vulnerabilities, compliance issues, duplicated effort, and inconsistent implementations.

How agentregistry fills the gaps

Learn how agentregistry allows you to overcome the challenge of managing AI artifacts across an organization.

Build, package, and deploy

Agentregistry comes with built-in tools to help developers manage the entire development cycle of AI artifacts, including agents, skills, and MCP servers. With agentregistry, you can quickly create, build, and package AI artifacts as Docker images. Then, run these images locally to verify that everything works and publish them to agentregistry to allow other developers to discover your artifacts.

Publishing artifacts to the registry is simple—developers can publish with a single command, eliminating complex deployment processes and reducing time-to-production. Once published, artifacts can be pulled from the registry and run in any environment instantly, whether that’s a developer’s local machine, a CI/CD pipeline, or a production cluster. This developer-focused workflow accelerates artifact and application development by providing access to a curated library of proven components while maintaining the flexibility to build custom solutions when needed.

Before deploying agents to agentic platforms, developers can use agentregistry to select the specific MCP servers, tools, and agent skills that they want to attach to their agent. This composition approach allows developers to build powerful, customized agents by combining proven, tested components from the registry.

Centralized governance and curation

Agentregistry enables organizations to establish comprehensive governance, maintain security standards, and ensure quality across all AI artifacts that are used within the company. Organizations can package and collect AI artifacts from any source—whether developed internally, sourced from open-source repositories, or obtained from third-party vendors—into a single, unified registry that the organization controls. This centralized approach eliminates fragmentation and provides a single source of truth for all AI artifacts.

You can also use agentregistry to implement review and approval workflows for artifacts before they become available to teams. These workflows help to maintain quality and security standards throughout the curation process, and ensure that agents, servers, and skills meet security, quality, and compliance requirements. Only artifacts that were approved and published to agentregistry can be deployed into environments, preventing untested or potentially insecure artifacts from reaching production environments.

Agentregistry also tracks and manages multiple versions of artifacts, allowing teams to use stable versions while new versions are being tested and approved.

Discovery

Agentregistry makes it easy for developers to discover, pull, and share AI artifacts, including MCP servers, agents, and skills, across the organization. Artifacts can be searched by name, description, tags, or metadata, making it easy for developers to find relevant artifacts for their projects. You can also track dependencies between artifacts, such as what agent uses which MCP server or skill.