How an AI Registry Accelerates Multi-Provider Agentic Systems
Learn how a registry layer simplifies managing AI agents, models, and data sources while maintaining governance and flexibility.
As AI systems become more modular, teams are building increasingly complex workflows using multiple agents and models—summarizers, classifiers, retrievers, planners—often served from different vendors, stacks, or environments.
Managing this growing sprawl of endpoints is becoming a new kind of operational challenge.
This post explores how a model registry can simplify the development and scaling of multi-provider agentic systems. We’ll look at its role in governance, routing, and experimentation, and how using a registry pattern brings structure and flexibility to otherwise brittle pipelines.
What is an AI Model Catalog?
A model catalog is a centralized registry of the large language models a provider supports or an organization has access to, across all providers, versions, and capabilities. It serves as a searchable directory that tells teams:
- Which models are available
- What they cost
- What features and constraints they support
A catalog typically includes metadata such as:
- Provider (e.g., OpenAI, Anthropic, Mistral)
- Model name and version
- Input/output token limits
- Supported modalities (text, vision, code, etc.)
This structure helps providers and organizations standardize discovery and governance of models.
A model catalog is typically static and suffers from stale/old data. It will list typically all available models, offline and online. It is usually up to the end user to figure out deployment methods/providers.
What is an AI Systems Registry?
There are two significant differences from regular model catalogs:
- An AI Registry is a catalog for not just models, but also AI agents and knowledgebases.
- The AI Registry knows where the actual deployment endpoing is and will route traffic to the live endpoint.
AI System Registries go beyond catalogs - they combine general-purpose LLMs, specialized agents (for reasoning, planning, or tool use), and external data sources like knowledge bases or retrieval APIs. Each of these components typically lives in its own environment and exposes a distinct API.
For example, you may use a general LLM API from OpenAI, but a content writer agent through OpenRouter with a guardrail agent on DigitalOcean fetching knowledgebases hosted on Pinecone.
An AI System registry serves as a unified index across all these building blocks—not just models. It tracks where each component lives, how to route to it, and under what configuration, environment, or version. This abstraction enables developers to build more modular, maintainable AI systems while preserving flexibility across stacks and providers.
How does an AI System Registry work?
An AI registry is a structured index of deployed model endpoints—live services that power downstream AI tasks.
Unlike static catalogs of models or datasets, a registry focuses on active, operational APIs. It helps teams answer questions like:
- Where is the summarizer for staging deployed?
- What version of our classifier is in production?
- Which endpoints use external providers vs. internal models?
It serves as a control layer for how requests are routed across environments, models, and providers.
Why It Matters in Agent-Based Architectures
Agentic systems frequently chain together multiple components: tools, retrievers, planners, classifiers, and language models. Each may live on a different stack or provider.
A registry helps address core challenges:
- Modular substitution: Swap a summarizer or classifier without rewriting orchestration code.
- Environment targeting: Route traffic to dev, staging, or production based on namespace. (Dev, Test, Prod, etc)
- Multi-provider fallback: Route requests to backups (e.g., internal → OpenRouter) during latency spikes or outages.
- Usage visibility: Trace calls and observe usage patterns across all expensive AI provider backends.
- Centralized governance: This is even more important at scale. You can enforce rate limits and resource limits at both organization and team level. Normally this governance is scatered across services/providers. You can now centrally define what resources are allowed to be accessed by whom. This is essential for maintaining control as usage scales across multiple teams.
- Access control and provisioning: An AI System registry abstraction layer allows you to define who gets access to which models, agents, datasources based on roles, teams, or environments (dev/staging/prod). This eliminates the risk of unauthorized or accidental usage of premium AI system and ensures compliance with internal and external policies.
Without a registry, these flows are often held together by hardcoded URLs and environment-specific logic. With a registry, you gain a stable routing layer with naming, versioning, and auditability built in.
How 638Labs AI System Registry helps you ship your AI powered apps
638Labs provides a gateway layer that allows developers to register any HTTP-accessible model (e.g., OpenAI, Together, Hugging Face, internal services) and route OpenAI-compatible requests through consistent endpoints.
By defining routes such as team/classifier-prod or team/classifier-testing, teams can manage traffic, version models, and swap providers—all without modifying client code.
638Labs gives you a centralized, live, online registry of all deployed models, agents, datasources.
Across organizations, the registry pattern supports a wide range of operational needs:
- Single implementation abstraction layer: Keep your app facing config stable, while changing AI providers as your needs evolve.
- Stable route naming: Abstract over vendor-specific model names with consistent, versioned routes.
your-org-name/your-agent-name-prodvsprovider-name/llm-version-xyz - Centralized access control: Manage who can call which routes, and under what conditions.
- Dynamic routing: Swap providers or endpoints without touching the orchestrator or client.
- Observability: Track performance, usage, and failures at the registry level.
- Environment isolation: Separate dev/staging/prod deployments via route naming or access controls.
- Unified discovery of deployed model/agent/knowledgebase: This is essential. Most model catalogs list available models, not deployed models. 638Labs is purpose built for live, deployed systems. Search and filter by type (agent, model, datasource), type of deployment (private or public), capabilities.
These capabilities become especially important in multi-team setups where governance, experimentation, and cost control must coexist.
Use case: AI Agentic Automation app used for Scheduling and Customer Order Management
Consider a business using AI to handle inbound scheduling or ordering via web forms, email, or chat. This is an asynchronous system—just structured or unstructured requests coming in and being processed by a pipeline of specialized agents:
-
Intake Handler
An agent monitors a shared inbox or intake form (e.g.orders@company.com). It uses a hosted model (e.g. OpenAI, Cohere) to extract key fields: customer name, request type, preferred date/time, or item details. -
Intent & Slot Filling
The structured data is sent to a classifier or tagger (e.g. on Hugging Face) to confirm user intent and ensure all required fields are filled (e.g., is this a reschedule, a cancellation, or a new order?). -
Planner Agent
A planner agent (hosted on live endpoints such as Together.ai) determines the next action—schedule the order, request clarification, or escalate to a human operator. -
Fallback Completion
If the planner stalls or data is incomplete, a fallback LLM (e.g. on OpenRouter) generates a clarification message or default response. -
Policy Checker
Before confirmation, the request is sent to an internal verifier agent to check for compliance with policies or SLAs (e.g., closed dates, max capacity, order limits). -
Fine-tuning Loop
Annotated outcomes (successful orders, missed cases) are periodically used to fine-tune your internal models (e.g., hosted on vLLM), improving accuracy over time.
Enter the Registry
Each of these components can be registered under a stable internal route name:
org/intake-parserorg/intent-detectororg/planner-prodorg/completion-fallbackorg/policy-checkorg/model-trainYou can setup your app in workflow automation frameworks such as n8n and you never have to touch the app code if you need to change providers.
This abstraction decouples orchestration logic from vendor details and unlocks:
- Flexibility — Swap or test providers without rewriting orchestration code
- Versioning — Track environments like
dev,staging, orprodby route - Governance — Centralized control of who calls what
- Observability — Unified logs and routing metrics
As modular AI systems grow, a registry becomes the glue that holds them together.
TL;DR
A model/agentic/knowledgebase registry provides the connective tissue for managing modern AI workflows:
- Centralizes endpoint discovery and naming
- Enables routing, fallback, and versioning
- Unifies discovery of various catalogs that host models, agents, datasources
- Decouples orchestration logic from provider-specific details
- Supports governance, experimentation, and scale
As AI systems grow more modular and span multiple providers, a registry layer becomes critical infrastructure—not just for speed, but for control and safety.
Learn more: https://638labs.com