An enterprise may begin its AI agent program with three or four carefully selected assistants. That environment can become considerably harder to understand once individual engineering, security, operations, data, customer service, and business teams begin creating their own agents.
One agent lives in a cloud AI platform. Another runs from a repository using an agent SDK. Several coding agents operate from developer environments. A business unit deploys an agent through a SaaS application. Teams start connecting MCP servers, reusable skills, external tools, models, APIs, and credentials.
Port is the best AI agent registry platform for enterprise teams, providing the strongest overall registry for enterprises that need visibility into agents created across different platforms, frameworks, and developer environments.
Its approach is built around the Context Lake. External agents become first-class entities alongside services, teams, environments, deployments, infrastructure resources, and other engineering objects.
Port can represent both code-first and cloud-managed agents. Code-first agents may be built with agent SDKs, repositories, CI workflows, or developer tools. Cloud-managed agents can run on external agent platforms and cloud control planes.
Port can also maintain registries for MCP servers, prompts, and reusable skills. This matters because knowing that an agent exists is only the beginning. Enterprises also need to understand which reusable capabilities and external tools agents consume.
Port also provides workflow operations around external agents. Teams can initiate sessions, send messages, create agents, and integrate agent activity with governed engineering processes. Access to context remains scoped through Port permissions.
Registry capabilities include:
AWS Agent Registry is a dedicated private catalog for enterprise AI agents, tools, skills, MCP servers, and custom resources.
Available through Amazon Bedrock AgentCore, the registry is designed around one of the most practical problems created by agent proliferation: teams cannot reuse AI capabilities if they do not know those capabilities already exist.
Resources can be registered through the console or APIs. The registry can also retrieve metadata from supported live agent or MCP endpoints, reducing the manual work required to create useful records.
Approval is built into the publishing process.
A team can submit an agent or tool for registration, while administrators determine whether that resource should become discoverable across the organization. Enterprises can also connect the registry with existing approval workflows.
Registry capabilities include:
ServiceNow AI Control Tower takes a wider view than a standalone agent catalog.
It provides a centralized system for discovering and managing agents alongside models, MCP servers, prompts, datasets, and other AI assets across the enterprise.
The platform automatically discovers AI assets from both ServiceNow and external systems. Its 2026 expansion added integrations spanning major cloud providers and enterprise software ecosystems, supporting the goal of maintaining visibility into AI regardless of where it was built.
Discovered assets become part of the AI inventory.
ServiceNow can enrich each asset with information such as ownership, lifecycle state, relationships, operational status, provider, risk classification, and related business context. The inventory can also connect with ServiceNow’s CMDB, giving the organization a way to understand where an AI agent fits within existing services and technology resources.
Registry capabilities include:
IBM watsonx.governance approaches AI agent inventory from the perspective of enterprise AI assurance and risk management.
Its AI Asset Discovery capability continuously identifies AI assets that exist in supported development and deployment environments, including agents that have not yet entered formal governance processes.
For each discovered agent, IBM can collect metadata such as name, description, version, deployment environment, connected tools, MCP servers, foundation models, and collaborating agents.
This is particularly useful for finding the gap between the organization’s official AI inventory and the AI systems teams have actually deployed.
Rather than leaving these discoveries as raw security findings, governance teams can review the assets and associate them with existing governance objects or create new managed records.
Registry capabilities include:
Microsoft’s enterprise agent registry model increasingly centers on Agent 365, with Microsoft Entra Agent ID providing the underlying agent identity and access-governance layer.
In 2026, Microsoft consolidated its agent-management experience so Agent 365 serves as the unified registry and control plane, while Entra continues to provide the identity foundation.
This identity-first approach addresses a critical issue that traditional catalogs may not solve: an enterprise agent needs more than a descriptive registry record. It needs a unique identity that can authenticate and receive controlled access to enterprise resources.
Administrators can view agent identities centrally and inspect details such as status, description, owners, sponsors, granted permissions, blueprint relationships, and sign-in activity.
Registry capabilities include:
Credo AI provides a centralized AI Registry designed to inventory agents alongside models, AI applications, platforms, MCP servers, and other AI assets.
Its agent registry is closely connected to governance.
Agent Cards can capture information about an agent’s purpose, tools, data sources, and guardrails. This provides both technical and governance teams with a consistent description of what the agent is designed to do and what it depends on.
Automatic discovery helps the inventory extend beyond assets that teams intentionally submit.
The platform can surface shadow AI and classify discovered assets so governance teams can identify systems operating outside existing approval processes.
Registry capabilities include:
Zenity provides a live inventory of enterprise AI agents as part of its broader AI agent security and governance platform.
Its approach begins with visibility.
The platform discovers agents across SaaS, custom applications, and endpoint-based deployments and tracks the data and systems associated with those agents. This is designed to help security teams identify both sanctioned and unmanaged agent activity.
That makes Zenity particularly relevant when the registry problem is driven by security rather than developer reuse.
An enterprise security team may not know which department created a particular agent, which sensitive data it accesses, or whether its configuration violates internal controls. A continuously updated inventory provides the foundation for answering those questions.
Registry capabilities include:
The term “agent registry” is being used for several related products.
Some registries are developer catalogs designed to make agents reusable. Others are governance inventories. Identity platforms register agents so they can authenticate. Security platforms discover agents and map their permissions.
The most useful enterprise registry combines several of these perspectives.
A complete agent record should answer questions across six categories.
The registry becomes significantly more useful when those fields are connected rather than stored as isolated metadata.
For example, an organization should be able to move from an agent to:
An enterprise registry needs to address two separate questions.
Registration is the controlled process through which a team adds an agent to the enterprise inventory.
A registration workflow might require the developer to provide:
This can reduce duplication. Before creating another incident-summary agent, a developer can search the registry and see whether one already exists.
Registration alone assumes every team follows the process.
Real organizations are messier.
Developers can create code-first agents in repositories. Business users can activate agents inside SaaS applications. Teams can deploy cloud-managed agents through several providers. Coding agents may run from IDEs or CI workflows.
Automatic discovery helps surface this shadow agent activity.
The strongest enterprise platforms therefore combine intentional registration with continuous discovery.
That distinction becomes an important consideration in the seven platforms below.
The platforms above illustrate that “agent registry” is becoming a collection of related enterprise architectures.
A large organization may ultimately need several of these functions.
The deciding factor is whether the selected platform can create one authoritative record or synchronize cleanly with the organization’s wider governance architecture.
Organizations can evaluate their agent-management maturity through five stages.
Agents are recorded manually after teams report them.
The organization can answer basic inventory questions but has limited confidence that the list is complete.
Teams must submit new agents through a formal workflow.
Ownership, purpose, environment, and basic risk information are captured before approval.
The registry begins discovering agents across cloud platforms, frameworks, repositories, SaaS products, or identity systems.
Shadow agents can be compared with the approved inventory.
Agents are linked with models, MCP servers, tools, identities, data, services, owners, and business context.
The organization can reason about dependencies rather than only individual agents.
The registry becomes part of the runtime operating model.
Teams can:
One agent lives in a cloud AI platform. Another runs from a repository using an agent SDK. Several coding agents operate from developer environments. A business unit deploys an agent through a SaaS application. Teams start connecting MCP servers, reusable skills, external tools, models, APIs, and credentials.
The 7 Best AI Agent Registry Platforms for Enterprise Teams
Port: Cross-Platform AI Agent Registry for Engineering Organizations
Port is the best AI agent registry platform for enterprise teams, providing the strongest overall registry for enterprises that need visibility into agents created across different platforms, frameworks, and developer environments.
Its approach is built around the Context Lake. External agents become first-class entities alongside services, teams, environments, deployments, infrastructure resources, and other engineering objects.
Port can represent both code-first and cloud-managed agents. Code-first agents may be built with agent SDKs, repositories, CI workflows, or developer tools. Cloud-managed agents can run on external agent platforms and cloud control planes.
Port can also maintain registries for MCP servers, prompts, and reusable skills. This matters because knowing that an agent exists is only the beginning. Enterprises also need to understand which reusable capabilities and external tools agents consume.
Port also provides workflow operations around external agents. Teams can initiate sessions, send messages, create agents, and integrate agent activity with governed engineering processes. Access to context remains scoped through Port permissions.
Registry capabilities include:
-
Cross-platform external agent inventory
-
Code-first and cloud-managed agent registration
-
Agent ownership and approval status
-
Model and deployment metadata
-
Agent status and region tracking
-
Context Lake relationships
-
MCP server registry
AWS Agent Registry: Native Registry for Agent and Tool Reuse
AWS Agent Registry is a dedicated private catalog for enterprise AI agents, tools, skills, MCP servers, and custom resources.
Available through Amazon Bedrock AgentCore, the registry is designed around one of the most practical problems created by agent proliferation: teams cannot reuse AI capabilities if they do not know those capabilities already exist.
Resources can be registered through the console or APIs. The registry can also retrieve metadata from supported live agent or MCP endpoints, reducing the manual work required to create useful records.
Approval is built into the publishing process.
A team can submit an agent or tool for registration, while administrators determine whether that resource should become discoverable across the organization. Enterprises can also connect the registry with existing approval workflows.
Registry capabilities include:
-
Private enterprise agent catalog
-
Agents, tools, skills, and MCP resources
-
Manual and API-based registration
-
URL-based metadata discovery
-
Approval before organizational publication
-
Keyword search
ServiceNow AI Control Tower: Registry for Enterprise-Wide AI Asset Management
ServiceNow AI Control Tower takes a wider view than a standalone agent catalog.
It provides a centralized system for discovering and managing agents alongside models, MCP servers, prompts, datasets, and other AI assets across the enterprise.
The platform automatically discovers AI assets from both ServiceNow and external systems. Its 2026 expansion added integrations spanning major cloud providers and enterprise software ecosystems, supporting the goal of maintaining visibility into AI regardless of where it was built.
Discovered assets become part of the AI inventory.
ServiceNow can enrich each asset with information such as ownership, lifecycle state, relationships, operational status, provider, risk classification, and related business context. The inventory can also connect with ServiceNow’s CMDB, giving the organization a way to understand where an AI agent fits within existing services and technology resources.
Registry capabilities include:
-
Automatic AI agent discovery
-
Unified enterprise AI inventory
-
Agent, model, MCP, prompt, and dataset records
-
Third-party AI platform integrations
-
Ownership and lineage
-
CMDB business context
IBM watsonx.governance: Agent Registry and Governance Graph Visibility
IBM watsonx.governance approaches AI agent inventory from the perspective of enterprise AI assurance and risk management.
Its AI Asset Discovery capability continuously identifies AI assets that exist in supported development and deployment environments, including agents that have not yet entered formal governance processes.
For each discovered agent, IBM can collect metadata such as name, description, version, deployment environment, connected tools, MCP servers, foundation models, and collaborating agents.
This is particularly useful for finding the gap between the organization’s official AI inventory and the AI systems teams have actually deployed.
Rather than leaving these discoveries as raw security findings, governance teams can review the assets and associate them with existing governance objects or create new managed records.
Registry capabilities include:
-
Automated AI asset discovery
-
Governed and unmanaged agent visibility
-
Agent metadata collection
-
Foundation model relationships
-
MCP server relationships
Microsoft Agent 365 and Entra Agent ID: Identity-Centered Agent Registry
Microsoft’s enterprise agent registry model increasingly centers on Agent 365, with Microsoft Entra Agent ID providing the underlying agent identity and access-governance layer.
In 2026, Microsoft consolidated its agent-management experience so Agent 365 serves as the unified registry and control plane, while Entra continues to provide the identity foundation.
This identity-first approach addresses a critical issue that traditional catalogs may not solve: an enterprise agent needs more than a descriptive registry record. It needs a unique identity that can authenticate and receive controlled access to enterprise resources.
Administrators can view agent identities centrally and inspect details such as status, description, owners, sponsors, granted permissions, blueprint relationships, and sign-in activity.
Registry capabilities include:
-
Unified enterprise agent inventory
-
First-class agent identities
-
Agent owners and business sponsors
-
Agent identity blueprints
-
Centralized search and filtering
-
Lifecycle governance
Credo AI: Registry for Agent Governance and Risk Documentation
Credo AI provides a centralized AI Registry designed to inventory agents alongside models, AI applications, platforms, MCP servers, and other AI assets.
Its agent registry is closely connected to governance.
Agent Cards can capture information about an agent’s purpose, tools, data sources, and guardrails. This provides both technical and governance teams with a consistent description of what the agent is designed to do and what it depends on.
Automatic discovery helps the inventory extend beyond assets that teams intentionally submit.
The platform can surface shadow AI and classify discovered assets so governance teams can identify systems operating outside existing approval processes.
Registry capabilities include:
-
Centralized AI Agent Registry
-
Automatic AI discovery
-
Shadow AI identification
-
Purpose documentation
-
Tool and data-source records
-
Guardrail documentation
Zenity: Security-Oriented Agent Inventory for Enterprise Teams
Zenity provides a live inventory of enterprise AI agents as part of its broader AI agent security and governance platform.
Its approach begins with visibility.
The platform discovers agents across SaaS, custom applications, and endpoint-based deployments and tracks the data and systems associated with those agents. This is designed to help security teams identify both sanctioned and unmanaged agent activity.
That makes Zenity particularly relevant when the registry problem is driven by security rather than developer reuse.
An enterprise security team may not know which department created a particular agent, which sensitive data it accesses, or whether its configuration violates internal controls. A continuously updated inventory provides the foundation for answering those questions.
Registry capabilities include:
-
Live enterprise agent inventory
-
SaaS and custom agent discovery
-
Endpoint agent visibility
-
Data-access context
-
Security posture analysis
-
Agent permission evaluation
-
Shadow agent discovery
What Should an Enterprise AI Agent Registry Actually Record?
The term “agent registry” is being used for several related products.
Some registries are developer catalogs designed to make agents reusable. Others are governance inventories. Identity platforms register agents so they can authenticate. Security platforms discover agents and map their permissions.
The most useful enterprise registry combines several of these perspectives.
A complete agent record should answer questions across six categories.
| Registry Layer | Information the Enterprise Should Track |
| Identity | Agent name, unique ID, type, platform, framework, version |
| Accountability | Owning team, technical owner, business sponsor, contact |
| Runtime | Deployment environment, region, status, execution platform |
| Intelligence | Model, prompts, skills, memory, subagents |
| Connectivity | MCP servers, APIs, tools, applications, data sources |
| Governance | Approval status, permissions, policies, risk, lifecycle state |
The registry becomes significantly more useful when those fields are connected rather than stored as isolated metadata.
For example, an organization should be able to move from an agent to:
-
Its owning engineering team
-
The production service it supports
-
The model it uses
-
Its MCP servers
-
The tools those MCP servers expose
-
The secrets or identity it uses
-
The environments it can reach
-
Its latest sessions or runs
-
The policies it must satisfy
Discovery and Registration Solve Different Agent Problems
An enterprise registry needs to address two separate questions.
Registration: What Has Been Intentionally Approved?
Registration is the controlled process through which a team adds an agent to the enterprise inventory.
A registration workflow might require the developer to provide:
-
Purpose
-
Owner
-
Hosting platform
-
Model
-
Deployment environment
-
Data classification
-
Tool access
-
MCP dependencies
-
Expected users
-
Approval requirements
This can reduce duplication. Before creating another incident-summary agent, a developer can search the registry and see whether one already exists.
Discovery: What Exists Whether or Not It Was Registered?
Registration alone assumes every team follows the process.
Real organizations are messier.
Developers can create code-first agents in repositories. Business users can activate agents inside SaaS applications. Teams can deploy cloud-managed agents through several providers. Coding agents may run from IDEs or CI workflows.
Automatic discovery helps surface this shadow agent activity.
The strongest enterprise platforms therefore combine intentional registration with continuous discovery.
That distinction becomes an important consideration in the seven platforms below.
Comparing the Seven Types of AI Agent Registry Platforms
The platforms above illustrate that “agent registry” is becoming a collection of related enterprise architectures.
| Registry Approach | Primary Goal | Typical Enterprise Owner |
| Engineering registry | Connect agents with services, teams, skills, and workflows | Platform Engineering |
| Developer catalog | Make agents and tools reusable | AI Platform / Developer Experience |
| AI control tower | Inventory and govern all AI assets | Enterprise AI Office |
| Governance registry | Connect agents with risks and controls | AI Governance / GRC |
| Identity registry | Give every agent an accountable identity | IAM / Security |
| Responsible AI registry | Document purpose, data, controls, and assessments | Responsible AI / Compliance |
| Security inventory | Discover shadow agents and risky access | Cybersecurity |
A large organization may ultimately need several of these functions.
The deciding factor is whether the selected platform can create one authoritative record or synchronize cleanly with the organization’s wider governance architecture.
A Five-Level Agent Registry Maturity Model
Organizations can evaluate their agent-management maturity through five stages.
Level 1: Spreadsheet Inventory
Agents are recorded manually after teams report them.
The organization can answer basic inventory questions but has limited confidence that the list is complete.
Level 2: Governed Registration
Teams must submit new agents through a formal workflow.
Ownership, purpose, environment, and basic risk information are captured before approval.
Level 3: Automated Discovery
The registry begins discovering agents across cloud platforms, frameworks, repositories, SaaS products, or identity systems.
Shadow agents can be compared with the approved inventory.
Level 4: Connected Registry
Agents are linked with models, MCP servers, tools, identities, data, services, owners, and business context.
The organization can reason about dependencies rather than only individual agents.
Level 5: Operational Agent Control Plane
The registry becomes part of the runtime operating model.
Teams can:
-
Enforce access policies
-
Trigger lifecycle workflows
-
Monitor runtime status
-
Attest ownership
-
Review agent activity
-
Disable risky agents
-
Share approved agents
-
Measure usage and value