Registration and Discovery Extension for Multi-Model Agents

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Table of Contents

1. Introduction

Intelligent agents built upon large language models (LLMs) have been widely deployed as core service components within AI-native systems. Conventional agent registration and discovery mechanisms expose static metadata, such as agent identifiers, service endpoints, authentication schemes, and supported capability catalogs.¶

These mechanisms operate under an implicit assumption that each agent is backed by only one inference model, and key service metrics including latency and functional capability remain stable and deterministic.¶

This foundational assumption, however, no longer holds true in production deployments. Most modern agents incorporate internal model routing capabilities, which dynamically select distinct foundation models at runtime to achieve balanced tradeoffs among inference latency, invocation cost, and output quality.¶

The broad adoption of intra-agent multi-model routing stems from two primary production scenarios.¶

Tiered Model Scheduling for Latency-Cost Balance: A single business-oriented agent integrates lightweight small-scale models, medium general-purpose models, and high-performance flagship models. Trivial routine queries are handled by low-latency, low-cost small models, while tasks involving sophisticated logical reasoning, long-document parsing, and mathematical computation are automatically routed to high-capacity flagship models. Downstream systems only need to connect to a unified service entry without manual model selection or switching.¶

Load-Adaptive Degradation and Fault Fallback: During traffic surges or GPU resource saturation, the routing module automatically offloads heavy-load requests from flagship models onto lightweight backup models to prevent request timeouts and sustain uninterrupted service. The full degradation and disaster recovery logic is fully transparent to external service discovery modules.¶

From an external perspective, such agents appear as a single logical service entity, yet their internal runtime execution behaviors exhibit substantial heterogeneity.¶

2. Problem Statement

3. Design Goals

The following design goals are defined for the extension mechanism:¶

• G1: Support representation of heterogeneous execution behavior within a single agent¶

• G2: Capture dynamic variability of service characteristics¶

• G3: Enable QoS-aware discovery and matching¶

• G4: Preserve single-endpoint abstraction¶

• G5: Maintain backward compatibility with existing systems¶

4. Protocol Overview

The system consists of the following entities:¶

• Agent: service provider with internal execution pipeline¶

• Registration Center: maintains metadata¶

• Service Consumer: performs discovery and invocation¶

Figure 1 illustrates the overall architecture of the extended registration and discovery framework.¶

+----------+         +-------------------+         +----------+
|          |         |                   |         |          |
|  Agent   |<------->| Registration      |<------->| Consumer |
|          | Register| Center            | Discover|          |
+----------+         +-------------------+         +----------+
       |                                           |
       |              Invoke                       |
       +-------------------------------------------+

5. Registration Extension

This section defines the extended registration format for agents with internal multi-model routing capabilities.¶

6. Model Update Procedure

Agents SHOULD report internal model lifecycle changes to the Registration Center. The following update types are defined:¶

• MODEL_ADDED: a new model has been added to the agent¶

• MODEL_REMOVED: an existing model has been removed from the agent¶

• MODEL_UPDATED: properties of an existing model have changed¶

• MODEL_DEGRADED: a model has entered a degraded state¶

Upon receiving an update notification, the Registration Center SHOULD update the corresponding Model Profile entries and notify affected consumers if necessary.¶

7. Discovery Extension

This section defines the extended discovery mechanism that enables consumers to perform QoS-aware service selection.¶

8. Backward Compatibility

Single-model agents are represented using a single default Model Profile entry. No protocol changes are required for existing implementations.¶

Registration Centers that do not support the extended format MAY ignore the Model-Profile-List field and treat the agent as a traditional single-model agent.¶

Consumers that do not support the extended format MAY continue to use the basic discovery mechanism without the QoS-aware fields.¶

9. Security Considerations

Model-level information MAY reveal sensitive system characteristics. Implementations SHOULD:¶

• protect registration and update messages using appropriate security mechanisms¶

• restrict exposure of internal model identities to authorized consumers¶

• prefer abstract capability representation where possible to avoid revealing sensitive model details¶

Registration Centers SHOULD implement access control policies to limit which consumers can access detailed Model Profile information.¶

10. IANA Considerations

This document does not require any IANA actions.¶

11. Conclusion

This document describes an emerging gap in current agent registration and discovery systems caused by internal multi-model routing within modern agent architectures. It identifies the need for execution-aware representation of agent behavior to enable accurate discovery and QoS-aware service selection in future AI-native networks.¶

The design goals, protocol overview, and extension mechanisms defined in this document provide a foundation for addressing this gap while maintaining backward compatibility with existing systems.¶

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