Registration and Discovery Extension for Multi-Model Agents
draft-zhu-agent-registration-discovery-00

agent2agent                                                       Y. Zhu
Internet-Draft                                              China Mobile
Intended status: Informational                               6 July 2026
Expires: 7 January 2027

      Registration and Discovery Extension for Multi-Model Agents
               draft-zhu-agent-registration-discovery-00

Abstract

   Existing agent registration and discovery mechanisms assume that each
   intelligent agent is backed by a single, static inference model.
   However, in real-world deployments, agents increasingly incorporate
   internal model routing mechanisms where multiple foundation models
   are used dynamically within a single agent based on task complexity,
   latency requirements, system load, and cost constraints.

   Although such agents are exposed externally as single service
   endpoints, their execution behavior is not static and may vary due to
   internal model selection logic that is not visible to external
   systems.  This leads to mismatches between registered metadata and
   actual service behavior, reducing the effectiveness of discovery and
   QoS-aware routing.

Note

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on 7 January 2027.

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Copyright Notice

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   document authors.  All rights reserved.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Problem Statement . . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Emergence of Internal Model Routing in Production
           Systems . . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.2.  Hidden Execution Heterogeneity  . . . . . . . . . . . . .   4
     2.3.  Lack of Transparency in Model Selection Behavior  . . . .   4
     2.4.  Impact on Discovery and QoS Matching  . . . . . . . . . .   4
     2.5.  Limitations of Static Agent-Level Representation  . . . .   5
     2.6.  Requirement for Execution-Aware Representation  . . . . .   5
   3.  Design Goals  . . . . . . . . . . . . . . . . . . . . . . . .   6
   4.  Protocol Overview . . . . . . . . . . . . . . . . . . . . . .   6
   5.  Registration Extension  . . . . . . . . . . . . . . . . . . .   6
     5.1.  Agent Registration Format . . . . . . . . . . . . . . . .   6
     5.2.  Model Profile . . . . . . . . . . . . . . . . . . . . . .   7
   6.  Model Update Procedure  . . . . . . . . . . . . . . . . . . .   7
   7.  Discovery Extension . . . . . . . . . . . . . . . . . . . . .   8
     7.1.  Discovery Request . . . . . . . . . . . . . . . . . . . .   8
     7.2.  Discovery Response  . . . . . . . . . . . . . . . . . . .   8
   8.  Backward Compatibility  . . . . . . . . . . . . . . . . . . .   8
   9.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   10. IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   11. Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .   9
   12. Normative References  . . . . . . . . . . . . . . . . . . . .   9
   13. Informative References  . . . . . . . . . . . . . . . . . . .   9
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .  10

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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

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2.1.  Emergence of Internal Model Routing in Production Systems

   In modern deployments of LLM-based agents, it is increasingly common
   for a single agent system to incorporate multiple foundation models
   internally.  These systems typically implement internal routing
   mechanisms that dynamically select models based on runtime conditions
   such as request complexity, system load, latency constraints, and
   cost optimization policies.

   Although multiple models may be involved in execution, this routing
   logic is generally not exposed to external registration or discovery
   systems.  As a result, the agent continues to be represented as a
   single static service entity.

2.2.  Hidden Execution Heterogeneity

   While an agent is registered as a single endpoint, its runtime
   behavior may vary significantly depending on which internal model is
   selected for a given request.

   Service-level characteristics such as latency, response quality, and
   cost are therefore not fixed attributes of the agent, but emergent
   properties of internal execution decisions.

   This leads to a situation where identical requests may receive
   different service-level outcomes depending on internal routing
   decisions that are not visible externally.

2.3.  Lack of Transparency in Model Selection Behavior

   Current registration and discovery mechanisms do not provide
   visibility into:

   *  how internal models are selected for different request types

   *  when fallback or degradation occurs

   *  how latency, cost, and quality trade-offs are enforced internally

   This lack of transparency prevents external systems from accurately
   reasoning about expected service behavior.

2.4.  Impact on Discovery and QoS Matching

   The absence of model-level or execution-level visibility leads to
   inaccurate discovery decisions.

   In particular:

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   *  agents may be selected without awareness of internal fallback
      behavior

   *  latency expectations may be violated due to hidden model switching

   *  cost-efficient routing decisions cannot be enforced externally

   *  high-complexity requests may be routed to insufficient capability
      paths under degradation

   These issues arise because current systems rely on static metadata
   that does not reflect dynamic execution behavior.

2.5.  Limitations of Static Agent-Level Representation

   Existing registration models assume that an agent can be described
   using a single, stable capability profile.

   However, in multi-model routing systems:

   *  capability is not static but context-dependent

   *  execution paths are dynamic and non-deterministic

   *  service-level characteristics vary across requests

   Therefore, static registration information is insufficient to
   accurately represent real-world agent behavior.

2.6.  Requirement for Execution-Aware Representation

   There is a requirement for registration and discovery systems to
   evolve beyond static capability descriptions and support
   representations that reflect:

   *  dynamic execution behavior within agents

   *  variability in service-level outcomes

   *  internal heterogeneity of model usage

   Such support is necessary to enable accurate service discovery and
   QoS-aware routing in environments where multiple models are used
   within a single agent system.

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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                       |
             +-------------------------------------------+

                       Figure 1: System Architecture

5.  Registration Extension

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

5.1.  Agent Registration Format

   The extended registration format includes the following fields:

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   *  Agent-ID: unique identifier for the agent

   *  Agent-Type: classification of the agent

   *  Endpoint: service endpoint URI

   *  Authentication: authentication information

   *  Skill-List: list of supported skills

   *  Model-Profile-List: list of internal model profiles (NEW)

5.2.  Model Profile

   Each Model Profile contains the following fields:

   *  Model-ID: unique identifier for the model within the agent

   *  Model-Type: model architecture or family

   *  Model-Version: version identifier

   *  Capability-Level: capability classification

   *  Latency-Class: expected latency classification

   *  Context-Length: maximum supported context length

   *  Cost-Class: cost classification

   *  Availability: availability status

   *  Supported-Skills: skills supported by this model

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

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   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.

7.1.  Discovery Request

   The extended discovery request includes the following fields:

   *  Required-Skill: the skill required for the request

   *  Maximum-Latency: maximum acceptable latency

   *  Minimum-Capability: minimum required capability level

   *  Minimum-Context-Length: minimum required context length

   *  Cost-Preference: preferred cost classification

7.2.  Discovery Response

   The extended discovery response includes the following fields:

   *  Agent-ID: identifier of the matched agent

   *  Matched-Model: identifier of the model that matches the request

   *  Capability-Level: capability level of the matched model

   *  Expected-Latency: expected latency for the matched model

   *  Endpoint: service endpoint for invocation

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.

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   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.

12.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/rfc/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.

13.  Informative References

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Author's Address

   Yidan Zhu
   China Mobile
   Beijing
   China
   Email: zhuyidan@chinamobile.com

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