---
title: "Agent Registry vs. Model Registry: What's the Difference?"
url: "https://atlan.com/know/ai-agent/agent-registry-vs-model-registry/"
description: "Compare agent registry vs. model registry: what each governs, how AWS Agent Registry works, and why most AI teams need both, not one or the other."
author: "Emily Winks"
author_role: "Data Governance Expert"
published: "2026-09-01"
updated: "2026-09-01"
---

---

A model registry governs a trained model: its versions, its promotion from staging to production, and the experiment lineage behind it. An agent registry governs something different: a running agent, the tools and skills it can call, and whether it's approved to operate at all. AWS Agent Registry reached general availability on August 31, 2026, giving the agent-registry side of this comparison a concrete, current reference point. Most teams building production AI eventually need both.

This guide covers what each registry tracks, where they overlap, how they work together, and the one question both leave open. If you already run a model registry and want to know whether an agent registry is a separate investment, the short answer is yes. Both are operational catalogs for approving what runs day to day, a different job from the regulatory AI-system inventory covered in [what is an AI registry](https://atlan.com/know/what-is-ai-registry/): that one is a compliance record for frameworks like the EU AI Act.

- A model registry versions trained model artifacts and gates their promotion to production.
- An agent registry catalogs agents, tools, skills, and MCP servers, and gates whether they're discoverable and approved to run.
- Both use an approval workflow that answers one question: is this approved to exist and run. Business-meaning accuracy sits outside that scope, a boundary this guide returns to.

---

## Quick facts: agent registry vs. model registry

| Dimension | Model registry | Agent registry |
|---|---|---|
| What it is | A versioned repository for trained ML model artifacts | A searchable catalog for agents, tools, skills, and MCP servers |
| What it governs | The model artifact and its lifecycle stage | A running agent and its callable capabilities |
| Approval gate | Stage promotion: development to staging to production | Publish, curate, approve workflow before discovery |
| Who typically owns it | ML and data science teams | Platform and AI engineering teams |
| Concrete 2026 example | MLflow, SageMaker Model Registry, Vertex AI Model Registry | AWS Agent Registry, GA August 31, 2026 |
| How resources are found | Registered model name and version lookup | Hybrid search, catalog browsing, native MCP endpoint |
| What it doesn't check | Whether the training data's business meaning is still current | Whether the agent's retrieved context is still accurate |

---

## Agent registry vs. model registry: what's the difference?

A model registry answers one question: which trained model, at which version, is approved to deploy. An agent registry answers a related but separate one: which agent, calling which tools and skills, is approved to run. A model registry versions a static artifact; an agent registry tracks an active system that calls other things and changes behavior between runs without a new training job.

Model registries matured first because MLOps had a decade's head start. Agent registries are catching up because the volume problem is new. According to Gartner (2026), the average Fortune 500 enterprise will run more than 150,000 AI agents by 2028, up from fewer than 15 in 2025. That growth is what turned "which agents do we have" into an inventory crisis, and it's the direct driver behind products like AWS Agent Registry.

Both are called "registries." Both gate approval. The difference is the noun after "registry," not the verb "register": a model registry's object is a trained artifact; an agent registry's object is a running system with dependencies on other systems. Getting that right matters more as [agent sprawl](https://atlan.com/know/ai-agent/agent-sprawl/) accelerates. According to IBM (2026), agent sprawl happens when agents are deployed without a unified strategy, and an ungoverned agent keeps acting, accessing data, and triggering workflows with no one aware it's still running. A registry is one input into [AI agent governance](https://atlan.com/know/ai-agent-governance/), covering what's cleared to exist and run; what an agent does after approval is a separate governance question.

---

## What is a model registry?

A model registry is a centralized system that versions, stores, and tracks machine learning models from training through production. It exists because a model isn't a one-time deliverable: teams retrain on new data, and without a registry, nobody can say with confidence which version is serving predictions, what data trained it, or who approved it.

According to an industry MLOps platform comparison (mlai.qa, 2026), MLflow remains the open-source baseline for experiment tracking and versioning, shipping as a library rather than a managed platform, while SageMaker Model Registry and Vertex AI Model Registry provide managed versioning and approval workflows scoped to their own clouds. None of the three catalog anything beyond the model artifact itself.

Atlan's own [model registry implementation guide](https://atlan.com/know/model-registry-implementation-guide/) covers the mechanics in full: schema design, artifact storage, and CI/CD integration. This page doesn't repeat that depth; what matters here is the object a model registry tracks.

### What a model registry typically tracks

- **Version and artifact storage** for model weights, configurations, and dependencies
- **Training metadata**, including dataset references, hyperparameters, and evaluation metrics
- **Lifecycle stage**, tracking a model's move from development to production
- **Approval and audit trail**, recording who promoted the model and when

A model registry can confirm a fraud-detection model passed every validation gate before production. Confirming that "fraud," as defined in training, still matches how the business defines it today is a separate question, one the registry is scoped away from by design. That scope boundary is the thread this page returns to. Programs built around [AI model governance](https://atlan.com/know/ai-readiness/ai-model-governance/) and a documented [responsible AI framework](https://atlan.com/know/ai-readiness/responsible-ai-framework-principles/) close part of it procedurally, but the registry stays scoped to the artifact.

---

## What is an agent registry?

An agent registry is a centralized, searchable catalog for the agents, tools, skills, and MCP servers an organization builds, with an approval workflow controlling what's discoverable. Where a model registry's object is a trained artifact, an agent registry's object is a runtime capability: something invocable, permissioned, and dependent on other registered resources.

AWS Agent Registry is the concrete instance to reason from. According to AWS (2026), [AWS Agent Registry](https://aws.amazon.com/blogs/machine-learning/manage-agents-tools-and-skills-at-scale-with-aws-agent-registry/) reached general availability on August 31, 2026, as a fully managed discovery service for MCP servers, tools, agents, agent skills, and custom resources. Records move through a publish, curate, discover lifecycle, and according to [AWS's developer documentation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/registry.html), each record is validated against its protocol schema and moves from draft to pending approval to approved, rejected, or deprecated. An **MCP Server** is one of the resource types the registry treats as first-class, alongside agents, tools, and skills. The registry's native MCP endpoint is the same [agent interoperability protocol](https://atlan.com/know/agent-interoperability-protocols/) teams use to [build MCP servers for enterprise data](https://atlan.com/know/ai-agent/how-to-build-mcp-servers-for-enterprise-data/) or an [MCP server for Salesforce](https://atlan.com/know/ai-agent/mcp-server-for-salesforce/): a registry record and an MCP endpoint describe the same resource from two angles, discovery and delivery.

AWS is unlikely to stay the only cloud vendor here, and enterprises running agents across more than one cloud tend to end up with a registry per platform, not one for the estate. Southwest Airlines, an early customer, put the reuse case plainly: "Registry cut duplicative development effort significantly and became the backbone of how we govern agentic AI," said Lauren Woods, CIO and EVP at Southwest Airlines (AWS Machine Learning Blog, 2026).

### What an agent registry typically tracks

- **Resource identity**, covering agents, tools, skills, and MCP servers as distinct objects
- **Ownership and authorization**, including who published and who may approve a record
- **Dependency and reuse signals**, surfacing what already exists before a rebuild
- **Approval and deprecation state**, the draft-to-approved-to-deprecated lifecycle

An agent registry can confirm an agent is approved to run and that a team reused an existing tool instead of duplicating it. Confirming that the context an agent retrieves at query time is still accurate is a separate question, and AWS's own documentation scopes the registry to discovery and approval rather than runtime accuracy. It's the same scope boundary as the model-registry section, now on the agent side, and it's why [agent identity](https://atlan.com/know/ai-agent/ai-agent-identity/) tracking and skill-level registries like [Atlan's agent skills registry breakdown](https://atlan.com/know/ai-agent/ai-agent-skills/agent-skills-registry/) stay scoped to "does this exist and who may use it," a different question from "is what it returns still true."

---

## Agent registry vs. model registry: head-to-head comparison

The clearest way to see where these categories diverge and overlap is dimension by dimension, using AWS Agent Registry and standard model-registry tooling as the reference points.

| Dimension | Model registry | Agent registry |
|---|---|---|
| Primary focus | Model artifact lifecycle | Agent and tool discovery and approval |
| Key stakeholder | ML engineer, data scientist | Platform engineer, AI engineering lead |
| What gets versioned | Model weights and configuration | Agent, tool, and skill definitions |
| Approval trigger | Passing validation and reaching a promotion gate | Publisher submission and curator review |
| Discovery mechanism | Name and version lookup | Hybrid search, catalog browsing, native MCP endpoint |
| Governing protocol | No single industry-wide protocol; tool-specific APIs | MCP server and agent record schemas |
| Failure mode when skipped | Untracked model versions, no audit trail | Duplicate agents rebuilt across teams |
| Concrete 2026 reference | MLflow, SageMaker Model Registry, Vertex AI Model Registry | AWS Agent Registry (GA August 31, 2026) |
| What it doesn't check | Whether the training data's meaning is current | Whether retrieved context is accurate at runtime |

**Example: two approvals, one shared dependency.** A platform team registers a fraud-detection model in SageMaker Model Registry after validation. Months later, a separate team builds an agent that calls that model to flag suspicious transactions, and that agent goes through its own approval in AWS Agent Registry before it's discoverable org-wide. Two registries, two approvals, one shared dependency.

Both approvals happened correctly, and both answered the same question: is this approved to exist and run. Whether "suspicious" is still defined the way the model was trained to detect it is a different question, one outside what [AI agent evaluation benchmarks](https://atlan.com/know/ai-agent-evaluation-benchmarks-and-metrics/) typically score, since those measure task performance, not definition drift. [Context observability for AI agents](https://atlan.com/know/ai-agent/ai-agent-observability/context-observability-for-ai-agents/) picks up closer to that gap, worth distinguishing from [context observability vs. data observability vs. LLM observability](https://atlan.com/know/ai-agent/ai-agent-observability/context-observability-vs-data-observability-vs-llm-observability/) before assuming any one covers it.

---

## How do agent registries and model registries work together?

The two categories compose rather than compete for budget: a registered agent frequently calls a registered model, and an organization with both gets stronger auditability than either provides alone.

### Composition: an agent calling a registered model

An agent registered in AWS Agent Registry lists a SageMaker-registered model as one of its callable tools. The model registry contributes version certainty; the agent registry contributes identity and permission, and its record shows which model version it depends on. The combined outcome is a dependency chain that's auditable end to end.

### Discovery: preventing duplicate builds on both sides

A model registry prevents a team from retraining a model that already passed validation. An agent registry prevents a second team from rebuilding an agent that already wraps that model. Together, they cut the two most common sources of wasted effort in a scaling AI program.

Start with a model registry when shipping your first production model. Add an agent registry once more than one team builds agents that call shared tools or models. Invest in both from day one on a greenfield agentic-AI build, since composition only works if both exist first. For what changes once agents span more than one cloud, see [how to govern AI agents across multiple clouds](https://atlan.com/know/ai-agent/how-to-govern-ai-agents-across-multiple-clouds/). A shared [agent context layer](https://atlan.com/know/ai-agent/agent-context-layer-design/), the tooling in [agent context layer tools compared](https://atlan.com/know/ai-agent/agent-context-layer-tools-compared/), and [securing multi-agent systems](https://atlan.com/know/ai-agent/ai-agent-governance/how-to-secure-multi-agent-systems-enterprise/) all matter once agents start calling each other's approved resources at scale.

---

## Do you need both an agent registry and a model registry?

Most organizations running production ML and more than one agent-building team need both: a model registry governs model versions and promotion; an agent registry governs which agents and tools exist and are approved to run. Skip the model registry and you're blind to model versions; skip the agent registry and you're blind to which agents exist, one of the risks in [AI agent risks and guardrails](https://atlan.com/know/ai-agent-risks-guardrails/). Either gap also leaves [agent memory](https://atlan.com/know/agent-memory-architectures/) unaddressed: what an agent learned across sessions is a governance surface outside both registries' scope.

- Need a model registry once more than one model reaches production and retraining creates version ambiguity.
- Need an agent registry once more than one team builds agents calling shared tools or models.
- Need both simultaneously on a greenfield agentic-AI platform, since composition is the default from the start.

Multicloud estates raise the stakes: each cloud typically brings its own registries, and the pattern of [context management in multi-agent systems](https://atlan.com/know/ai-agent/ai-agent-context/context-management-multi-agent-systems/) inside one platform has to be rebuilt separately for each one. Skipping the coordination layer between registered components is the mistake documented in [agent harness failures and anti-patterns](https://atlan.com/know/agent-harness-failures-anti-patterns/). Standing up either program benefits from the structure in [how to build an AI center of excellence](https://atlan.com/know/ai-agent/ai-agent-governance/how-to-build-ai-center-of-excellence/), since someone needs to own curation on both sides.

---

## What doesn't an agent or model registry check?

Both registries answer the same question for a different object: is this approved to exist and run. A model registry asks it of a trained artifact; an agent registry asks it of a running system. Whether the business meaning either one depends on is still accurate is a separate question, scoped to a different layer entirely.

That's the layer [Atlan](https://atlan.com/know/what-is-context-layer/)'s context layer addresses. Atlan's [Enterprise Data Graph](https://atlan.com/know/enterprise-data-graph/), built on the distinction covered in [context graph vs. knowledge graph](https://atlan.com/know/context-graph-vs-knowledge-graph/), and [Context Repos](https://atlan.com/know/why-ai-agents-need-an-enterprise-context-layer/) work underneath the approval workflow: they supply the governed meaning, ownership, and freshness a model trained against and an agent retrieves at runtime, delivered through the Atlan MCP server so the same governed answer reaches an agent regardless of which registry approved it. A skill can pass every approval gate and still reason from a stale [semantic layer](https://atlan.com/know/semantic-layer/) definition. Registry integrity and context accuracy are different audits.

This distinction, applied specifically to AWS Agent Registry, is covered in full in [does an agent registry need a context layer](https://atlan.com/know/ai-agent/context-layer/do-agent-registries-need-a-context-layer/). For [what an AI agent registry](https://atlan.com/know/ai-agent/what-is-an-ai-agent-registry/) should mean for your organization, start there. Building the underlying layer starts with [how to implement an enterprise context layer for AI](https://atlan.com/know/how-to-implement-enterprise-context-layer-for-ai/) and the discipline of [context engineering](https://atlan.com/know/what-is-context-engineering/) that keeps it current as both registries grow.

---

## Two registries, one blind spot

The useful question was never which registry to pick. A model registry and an agent registry govern different objects, and scaling production AI needs both, the same way it needs version control and access control: they check different failure modes. As agent counts climb toward Gartner's projected 150,000 per enterprise by 2028, the number of registries an organization runs will keep multiplying by cloud and by artifact type, not shrink to one. What has to stay constant underneath that multiplication is the business meaning both registries assume is correct, the piece every registry leaves for another layer to confirm.

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---

## FAQs about agent registry vs. model registry

### 1. What is the difference between a model registry and an agent registry?

A model registry versions and approves trained model artifacts: weights, training data references, promotion stage. An agent registry catalogs and approves running agents, tools, skills, and MCP servers: identity, ownership, dependencies. Different objects, similar approval mechanism.

### 2. Why do I need an agent registry if I already have a model registry?

A model registry has no visibility into which agents exist or what tools they call. Once more than one team builds agents, especially agents calling shared models or tools, an agent registry prevents duplicate builds and gives platform teams one place to approve and discover what exists.

### 3. What belongs in an AI agent registry record?

A typical record includes the resource's identity and type, its owner, its access settings, its dependencies, and its approval state. AWS Agent Registry validates records against protocol-specific schemas for MCP servers and agents.

### 4. Does AWS Agent Registry replace SageMaker Model Registry?

No. AWS Agent Registry catalogs agents, tools, skills, and MCP servers, while SageMaker Model Registry versions trained model artifacts. An agent registered in AWS Agent Registry commonly calls a model tracked separately in SageMaker Model Registry; the two are complementary.

### 5. Can an agent be registered without the model it calls being registered too?

Yes, and this is a common gap. An agent registry approves the agent as a runtime resource without requiring every model it calls be separately version-controlled. Organizations running both registries need a process connecting an agent's record to the model entries it depends on.

### 6. What is the difference between a model registry and a model catalog?

A model registry focuses on versioning and deployment lifecycle management for active models. A model catalog provides broader discovery, connecting models to business context, lineage, and governance policies. Many organizations use a registry for MLOps workflows and a catalog for enterprise-wide visibility.

### 7. How do model registries and agent registries fit into a multicloud AI stack?

Each cloud provider typically ships its own agent and model registry, so a multicloud organization runs more than one of each rather than a unified system. The registries don't reconcile across platforms; that happens at a layer above them.

### 8. Does a registry guarantee the AI agent is using accurate data?

No. Both registry types confirm a resource is approved to exist and run. Neither verifies that the business definitions or freshness of the data an agent retrieves are still correct. That check happens at the context layer, not the registry.

---

## Sources

1. Manage agents, tools and skills at scale with AWS Agent Registry, AWS Machine Learning Blog, 2026. https://aws.amazon.com/blogs/machine-learning/manage-agents-tools-and-skills-at-scale-with-aws-agent-registry/
2. AWS Agent Registry: Discover and manage agents, tools, and resources, AWS Bedrock AgentCore Developer Guide, 2026. https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/registry.html
3. Gartner Identifies Six Steps to Manage AI Agent Sprawl, Gartner Newsroom, 2026. https://www.gartner.com/en/newsroom/press-releases/2026-04-28-gartner-identifies-six-steps-to-manage-artificial-intelligence-agent-sprawl
4. MLOps Platform Comparison 2026: Kubeflow vs MLflow vs SageMaker vs Vertex AI vs Databricks, mlai.qa, 2026. https://mlai.qa/mlops-platform-comparison-2026/
5. What is AI Agent Sprawl?, IBM Think, 2026. https://www.ibm.com/think/topics/ai-agent-sprawl