



NMRG                                                        M. A. Jadoon
Internet-Draft                                              S. Robitzsch
Intended status: Informational                   InterDigital Europe Ltd
Expires: 3 September 2026                                C. J. Bernardos
                                        Universidad Carlos III de Madrid
                                                            2 March 2026


  Agentic AI Architectural Principles for Autonomous Computer Networks
          draft-jadoon-nmrg-agentic-ai-autonomous-networks-00

Abstract

   Agentic AI systems combine planning, reasoning, tool invocation, and
   feedback loops to pursue system-defined goals with a controlled
   degree of autonomy.  In networking, this enables an evolution from
   statically configured automation toward goal-driven closed-loop
   operations spanning multiple protocol layers and administrative
   domains.

   This document introduces architectural principles for "agentic
   augmentation" of the existing layered protocol stack as represented
   by the Internet protocol suite (IP suite).  The key concept of the
   proposed principles is that deterministic protocol layering remains
   intact for interoperability, while AI Agents are introduced as first-
   class entities at each IP suite layer and are coordinated by one or
   more agent controllers via agentic methods and procedures.

   The purpose of this document is to initiate discussion within the
   research community on agentic networking.  It identifies
   architectural research challenges that should be discussed to enable
   the addition of one or more AI Agents at one or more IP suite layers
   with the goal to allow AI Agents to improve the behaviour of a layer
   through reasoning with AI Agents at the same or other IP suite
   layers.

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
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.






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   Internet-Drafts are draft documents valid for a maximum of six months
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   This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology and Conventions . . . . . . . . . . . . . . . . .   3
   3.  Baseline: IP Suite Layered Protocol Stack . . . . . . . . . .   4
   4.  Proposed Agentic Augmentation at to Layered Approach  . . . .   4
     4.1.  5.1.  Per-Layer Agent Nets and Per-Layer Controllers  . .   5
     4.2.  5.3.  Relationship to the Deterministic Stack . . . . . .   5
   5.  Functional Workflow and Reference Points  . . . . . . . . . .   6
     5.1.  6.1.  Controller and Agent Functional Blocks  . . . . . .   6
     5.2.  6.2.  Reference Points (Interfaces) . . . . . . . . . . .   6
   6.  Challenges Motivating Interoperable Work  . . . . . . . . . .   9
   7.  Security, Safety, Determinism, and Accountability
           Considerations  . . . . . . . . . . . . . . . . . . . . .   9
   8.  Relationship to Existing IETF/IRTF Work . . . . . . . . . . .  11
   9.  Recommendations and Next Steps for IETF Discussion  . . . . .  11
   10. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  11
   11. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  11
   12. Normative References  . . . . . . . . . . . . . . . . . . . .  11
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  12

1.  Introduction

   Computer networks have long pursued higher automation for self-
   configuration, self-healing, self-optimization, and self-protection.
   Recent advances in AI, including agentic AI systems, enable a shift
   from isolated automation approaches toward distributed, goal-driven
   closed-loops that can plan multi-step actions, invoke tools, adapt to
   feedback, and coordinate with peers to improve the behaviour of
   existing or new protocols.



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   However, multi-agent deployments are typically proprietary: there is
   no widely adopted, vendor-independent approach for naming, reaching,
   discovering, and coordinating AI Agents that act on network state
   across multiple protocol layers and domains.

   This document is intended to seed IETF discussion by proposing a
   architectural principles and an interface model for "agentic
   augmentation" of the existing layered OSI stack.  The objective is
   not to replace the deterministic behavior of protocols used for
   interoperability and packet transport.  Instead, the architecture
   introduces AI Agents at each layer, coordinated by orchestrators, so
   that closed-loop automation can be pursued in a structured and
   governable way.

2.  Terminology and Conventions

   AI Agent (AIA): A software entity capable of pursuing goals by
   reasoning and planning, and by invoking tools (APIs, protocols,
   controllers, knowledge bases) to observe state and perform actions.

   Agentic AI System: A system composed of one or more AI Agents and
   optional controller(s) that performs goal-directed operations over
   time by using available tools and feedback loops.

   Agent Controller: A logical function that accepts an input request/
   goal, performs task decomposition and assignment, coordinates one or
   more AI Agents, and produces outputs or verified actuation outcomes.
   Controllers may be per-layer (distributed) or centralized.

   Agent Net: A set of AI Agents associated with a specific layer of the
   stack, together with their coordination logic and tools for that
   layer.

   IP Suite Stack Interfaces: The conventional (non-agentic) interfaces
   between adjacent layers (e.g., a transport interface exposed to
   applications), and the conventional control/management interfaces
   that configure or observe a layer (e.g., socket APIs, YANG/NETCONF/
   RESTCONF, routing protocol configuration, telemetry streams).

   Tool: Any callable capability used by an AI Agent to ground reasoning
   or execute actions (e.g., retrieve telemetry, push configuration,
   initiate diagnostics, query a digital twin, call a controller).









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3.  Baseline: IP Suite Layered Protocol Stack

   Layering is central to interoperability.  In a conventional layered
   stack, upper layers invoke services from lower layers using static,
   well-defined interfaces.  In practice, deployed systems follow the
   Internet protocol suite as shown in Figure 1, commonly represented by
   Application, Transport, Internet, and Link/Access and physical
   layers.  Nevertheless, the layered abstraction remains a useful
   conceptual baseline for discussing where automation and closed-loop
   decision functions are inserted.

              +------------------+
              |   Application    |
              +------------------+
              |    Transport     |
              +------------------+
              |     Internet     |
              +------------------+
              |   Link / Access  |
              +------------------+
              |     Physical     |
              +------------------+

            Figure 1: IP Suite Layered Stack

4.  Proposed Agentic Augmentation at to Layered Approach

   The proposed approach is hybrid: the deterministic layered protocol
   stack remains the baseline for interoperability and packet transport,
   while agentic functions are introduced alongside each layer.

   In the proposed architecture, each layer contains:

   *  An Agent Net that can interpret intents relevant to that layer,
      gather context (telemetry/state), reason, propose actions, and
      execute actions through the layer's tools.  Agent Net may have one
      or more AIAs for different purposes within in the layer.  For
      example, a layer may have different agents for congestion control,
      QoS adaptation, policy and compliance and intent-parsing.

   *  An agent controller or simply controller manages task
      decomposition, assignment, guardrails and governance.  A
      controller may or may not be an AIA.

   The proposed model targets a "structured autonomy" property:
   autonomous actions are possible, but are constrained through explicit
   reference points, policies, authentication/authorization, and
   auditable workflows.



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4.1.  5.1.  Per-Layer Agent Nets and Per-Layer Controllers

   In this option, each layer has its own Agent Net and its own
   controller.  This aligns with operational decomposition (e.g.,
   separate ownership or tooling per layer), and supports scaling and
   fault isolation.

   Figure 2a illustrates the concept.

     +------------------+    +-------------------------+    +---------------------+
     |   Application    |<-->|  Agent Net (App)        |<-->|  Controller (App)   |
     +------------------+    +-------------------------+    +---------------------+
     |    Transport     |<-->|  Agent Net (Transport)  |<-->|  Controller (Trans) |
     +------------------+    +-------------------------+    +---------------------+
     |     Internet     |<-->|  Agent Net (Internet)   |<-->|  Controller (Int)   |
     +------------------+    +-------------------------+    +---------------------+
     |  Link / Access   |<-->|  Agent Net (Link)       |<-->|  Controller (Link)  |
     +------------------+    +-------------------------+    +---------------------+
     |    Physical*     |<-->|  Agent Net (Phy)*       |<-->|  Controller (Link)* |
     +------------------+    +-------------------------+    +---------------------+

     Figure 2a: Proposed Stack with Per-Layer Agent Nets and Per-Layer Controllers

   The Physical layer is shown for completeness and includes data-link
   and access technologies.  Some aspects of access technologies may be
   outside IETF scope and would typically be accessed as external tools/
   controllers rather than specified by IETF protocols.

4.2.  5.3.  Relationship to the Deterministic Stack

   The hybrid architecture is intended to preserve deterministic
   interoperability of the underlying stack:

   *  Deterministic inter-layer service interfaces (e.g., a transport
      service offered to applications) remain unchanged.

   *  AI Agents do not replace protocols; they invoke tools that already
      exist (or will be defined) to observe and actuate layer behavior.

   *  The architecture explicitly separates (a) packet transport and
      protocol interoperability, from (b) goal-driven automation logic.

   This separation is central to making agentic automation governable,
   testable, and incrementally deployable.







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5.  Functional Workflow and Reference Points

5.1.  6.1.  Controller and Agent Functional Blocks

   A generic agentic workflow can be represented by the following
   functional blocks:

   Controller:

   *  I/O Engine: handles intent/goal/request intake and output
      formatting.

   *  Discovery: finds relevant agents, tools and capabilities.

   *  Task Assignments: decomposes tasks and assigns them to agents
      (planning).

   AI Agent:

   *  Reasoning: interprets tasks, plans steps, and maintains context.

   *  Synthesis: composes intermediate results into coherent outputs.

   *  Execution: invokes tools and performs actions.

   *  Reinforcement: learns/adapts from feedback.

   *  Conflict Resolution: resolves competing proposals/actions among
      agents or across layers.

   Figure 3 illustrates this functional view and explicitly labels
   reference points that are candidates for interoperable definition.

5.2.  6.2.  Reference Points (Interfaces)

   The following reference points are identified:

   C_I (Controller Input): Input request/goal interface into the
   controller.

   C_O (Controller Output): Output response (including action
   confirmation, explanation, or result summary) from the controller.

   C_E (Controller Execution Feedback): Interface for execution-related
   status and feedback flow between controller and agent reasoning
   context (e.g., progress, intermediate outcomes, updated constraints).





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   AIA_E (Agent Execution / Task Assignment): Task assignment and
   execution coordination between controller and AI Agent execution
   function.

   AIA_R (Inter-AI-Agent Reasoning/Context Exchange): Interface for AI
   Agents to exchange context, negotiate, and coordinate during
   reasoning and planning.

   AIA_CR (Inter-AI-Agent Conflict Resolution): Interface supporting
   detection, signaling, and resolution of conflicts among AI Agents
   (including conflicts spanning layers).








































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        +------------------+   C_I   +--------------------------------------+
        |   Input Request  |-------> |              Controller              |
        +------------------+         |                                      |
                                     |   +----------+   +----------+        |
        +------------------+   C_O   |   | I/O      |-->| Discovery |----+  |
        | Output Response  |<------- |   | Engine   |   +----------+    |   |
        +------------------+         |   +----------+                   |   |
                                     |        ^ C_E                     |   |
                                     |        |                         v   |
                                     |   +----------------+     +-----------+
                                     |   | Task           |     | Task      |
                                     |   | Assignments    |---->| Assignment|
                                     |   +----------------+     +-----------+
                                     +----------------------------------|---+
                                                                        |
                                                                       AIA_E
                                                                        |
                                     +----------------------------------v---+
                                     |                 AI Agent              |
                                     |                                       |
                                     |   +-----------+    +-----------+      |
                       AIA_R  <----> |   | Reasoning |<-->| Synthesis  |      |
                                     |   +-----------+    +-----------+      |
                                     |        ^                 ^            |
                                     |        |                 |            |
                                     |   +------------------+   |            |
                       AIA_CR <----> |   | Conflict          |---+            |
                                     |   | Resolution        |                |
                                     |   +------------------+                |
                                     |                                       |
                                     |   +-----------+    +----------------+ |
                                     |   | Execution |<-->| Reinforcement  | |
                                     |   +-----------+    +----------------+ |
                                     |           |                           |
                                     |           v                           |
                                     |        Tools / Controllers            |
                                     +---------------------------------------+

          Figure 3: Functional Workflow of Agentic AI Systems with Reference Points (Conceptual)

   The intent is not to prescribe a single protocol, but to establish
   common reference points so that multiple protocol proposals (e.g.,
   agent-to-agent, agent discovery, tool invocation) can be evaluated
   against a consistent architectural model.







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   In this document, the term "CX interface" refers to the controller
   external interfaces (primarily C_I and C_O), and the term "AIAX
   interfaces" refers to the agent-related interfaces (C_E, AIA_E,
   AIA_R, AIA_CR) shown above.

6.  Challenges Motivating Interoperable Work

   This section summarizes key challenges observed in multi-layer,
   multi-agent deployments that motivate IETF discussion:

   *  *Lack of a common way for AIAs to identify and reach each other*:
      Agents operate at different technical layers and environments.
      There is no consistent way to name, locate, and address agents
      across networks and platforms.  This may results in fragile
      interoperability, high integration cost, and limited scaling in
      multi-vendor environments.

   *  *Limited service discoverability and failure signaling for AIAs*:
      Agents need to discover other agents, determine if a service
      exists, and understand why communication failed.  This may result
      in silent failures, unbounded retries, incorrect decisions from
      incomplete information, and reduced trust in automation.

   *  *Insufficient data provisioning for training and adaptation*:
      Agents need data not only for initial training but also during
      operation or for updates.  Agents need consistent approaches for
      accessing additional data and exchanging learning-relevant
      information in consistent formats.

   *  *Distributed security token and access control management*:
      Agents require credentials to prove identity, access resources,
      and act on behalf of users/systems.  Token creation, scoping,
      renewal, and revocation are not unified across agent types and
      layers, complicating audit and compliance.

   These challenges are compounded by multi-domain operation, different
   trust boundaries, and heterogeneous tooling.

7.  Security, Safety, Determinism, and Accountability Considerations

   Agentic AI systems introduce architectural risks beyond those
   associated with traditional automation or centralized controllers.

   *  *Sustainability*:
      Continuous inference, multi-agent coordination, and expanded
      telemetry exchange may increase energy consumption and management-
      plane load.  Architectural mechanisms prefer bounding control-
      plane amplification and computational overhead.



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   *  *Security*:
      Agentic systems expand the attack surface through:

      -  Tool invocation chains that can enable privilege escalation.

      -  Cross-agent coordination interfaces that can propagate
         compromise.

      -  Persistent memory and context that can be subject to poisoning.

      -  Model-driven reasoning that can be influenced by malicious or
         malformed inputs.

      Compromise of a single agent can have cascading impact across
      layers if coordination boundaries are not explicitly constrained.

   *  *Deterministic Guardrails*:
      Networking environments require bounded and predictable behavior.
      Agentic augmentation is intended to preserve deterministic
      protocol invariants and stability properties of the underlying
      stack.  Guardrail mechanisms can include:

      -  Policy-constrained action spaces.

      -  Pre-execution validation against safety envelopes.

      -  Transactional rollback and state checkpointing.

      -  Rate-limited reconfiguration to prevent oscillation.

      -  Deterministic conflict resolution hierarchies.

      -  Human override and escalation triggers.

      These mechanisms ensure that autonomy remains structured and does
      not undermine protocol correctness or service guarantees.

   *  *Accountability*:
      Autonomous decisions are expected to be traceable to inputs,
      policies, and authorization context.  Auditable execution logs and
      explainability hooks are required to support compliance,
      debugging, and operational trust.

   This document does not define specific mechanisms but asserts that
   structured autonomy and deterministic safety envelopes are
   foundational architectural requirements for agentic networking.





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8.  Relationship to Existing IETF/IRTF Work

   This document is complementary to multiple ongoing efforts in the
   IETF and IRTF that touch on autonomic networking, AI agents for
   network management, and agent communication.  These efforts can be
   used as inputs when refining protocols that realize the reference
   points in this draft.

9.  Recommendations and Next Steps for IETF Discussion

   The following near-term discussion items are proposed:

   1.  Discuss an agree on the terminology for the agentic AI
       architecture

   2.  Orchestration reference framework and reference points: Refine
       the CX and AIAX reference points and their semantics.  Determine
       what "minimum interoperability" means for each reference point in
       a multi-vendor environment.

   3.  Agent registration, discovery, and lifecycle: Identify
       interoperable mechanisms for agent discovery and capability
       advertisement, including failure signaling and versioning.

   4.  Security tokens, authorization, and auditability: Identify how
       agents authenticate, obtain scoped authorization, and produce
       auditable action traces when acting on network state.

   5.  Layer-by-layer automation building blocks: Use the architecture
       to guide follow-on documents that focus on automation and closed-
       loop control within specific layers (e.g., routing layer,
       transport layer, service layer), without conflating those with
       the inter-agent interface problem.

10.  IANA Considerations

   TBD

11.  Acknowledgements

   TBD

12.  Normative References

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




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

Authors' Addresses

   Muhammad Awais Jadoon
   InterDigital Europe Ltd
   London
   United Kingdom
   Email: muhammad.awaisjadoon@interdigital.com


   Sebastian Robitzsch
   InterDigital Europe Ltd
   London
   United Kingdom
   Email: sebastian.robitzsch@interdigital.com


   Carlos J. Bernardos
   Universidad Carlos III de Madrid
   Madrid
   Spain
   Email: cjbc@it.uc3m.es


























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