



Network Working Group                                             Y. Cui
Internet-Draft                                       Tsinghua University
Intended status: Informational                                     C. Du
Expires: 8 January 2026                          Zhongguancun Laboratory
                                                             7 July 2025


     Task-oriented Coordination Requirements for AI Agent Protocols
                       draft-cui-ai-agent-task-00

Abstract

   AI agent communication requires intelligent task level coordination
   to manage dynamic workloads across large-scale, heterogeneous
   networking environments.  This draft proposes general requirements
   for an agent protocol to enable autonomous task coordination at
   scale, including dynamic task discovery, negotiation, and context-
   aware scheduling with real-time adaptability.

Status of This Memo

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   This Internet-Draft will expire on 8 January 2026.

Copyright Notice

   Copyright (c) 2025 IETF Trust and the persons identified as the
   document authors.  All rights reserved.











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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Purpose . . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.2.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   3
     1.3.  Task Coordination Framework . . . . . . . . . . . . . . .   3
   2.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Necessity . . . . . . . . . . . . . . . . . . . . . . . . . .   4
     3.1.  Task Complexity . . . . . . . . . . . . . . . . . . . . .   4
     3.2.  Resource Optimization . . . . . . . . . . . . . . . . . .   5
     3.3.  Quality of Service  . . . . . . . . . . . . . . . . . . .   5
     3.4.  Dynamic Adjustment  . . . . . . . . . . . . . . . . . . .   5
   4.  Protocol Requirements . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Task Description  . . . . . . . . . . . . . . . . . . . .   5
     4.2.  Task State  . . . . . . . . . . . . . . . . . . . . . . .   6
     4.3.  Communication Mechanism . . . . . . . . . . . . . . . . .   6
     4.4.  Context Sharing . . . . . . . . . . . . . . . . . . . . .   8
     4.5.  Exception Handling  . . . . . . . . . . . . . . . . . . .   8
   5.  Existing Protocol Analysis  . . . . . . . . . . . . . . . . .   8
   6.  Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .   9
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   9.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   9
     9.1.  Normative References  . . . . . . . . . . . . . . . . . .   9
     9.2.  Informative References  . . . . . . . . . . . . . . . . .   9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10

1.  Introduction

1.1.  Purpose

   With the rapid advancements of AI technologies and their
   applications, AI agents utilizing Large Language Models (LLMs) have
   emerged as a pivotal direction in global technological evolution and
   market development.  The single-agent systems exhibit inherent
   limitations when addressing complex tasks in dynamic environments,
   the efficient multi-agent collaboration for complex task completion
   has garnered increasing attention, wherein task-oriented coordination
   constitutes a critical component of standardized multi-agent systems.



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   This document examines the requirements for standardizing AI Agent
   protocols to support task coordination in multi-agent systems.

1.2.  Terminology

   Task:
      ISO/IEC 22989, task is actions required to achieve a specific
      goal.  These actions can be physical or cognitive.  For instance,
      computing or creation of predictions, translations, synthetic data
      or artefacts or navigating through a physical space.

   Shared Message Pool:
      A pool where agents publish structured messages and subscribe to
      relevant messages based on their profiles.

   Coordinator Agent:
      An agent that receives tasks and decomposes or distributes tasks
      to other agents.

   Execution Agent:
      An agent responsible for executing tasks distributed by the
      Coordinator Agent.

   Normative Language:
      The key words "MUST", "REQUIRED", and "SHOULD" in this document
      are to be interpreted as described in [RFC2119].

1.3.  Task Coordination Framework

                                                             +---------+
                                        +------------------->| Agent X |
                                        |2.Task1 distributed +---------+
                                        |                        |
                                        |                        |
                                        |                        |
     +---------+  1.Task submitted  +---------+ 3.Task1 completed|
     |   Task  |------------------->|         |<-----------------+
     | Invoker |<------------------ | Agent A |<-----------------+
     +---------+  4.Task completed  +---------+ 3.Task2 completed|
                                        |                        |
                                        |                        |
                                        |                        |
                                        |2.Task2 distributed +---------+
                                        +------------------->| Agent Y |
                                                             +---------+

                   Figure 1: Task Coordination Framework




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   The system operates as follows: when a task invoker submits a task to
   Agent A (Coordinator), the agent performs task decomposition and
   distributes task 1 and task 2 to Agents X (Execution) and Y
   (Execution) respectively.  Upon receiving completion notifications
   from both agents, Agent A aggregates the results and delivers the
   final output back to the originating user.

2.  Use Cases

   Some typical use cases in which multiple agents work together to
   complete tasks:

   *  High throughput tasks: There are many tasks that have high
      bandwidth requirements.  For example, in the collaborative
      framework for coordinating heterogeneous embodied agents-
      specifically, the robot dogs and drones-in a wide-area public
      network, the drone is assigned for wide-area surveillance and task
      delegation, while functionally specialized robot dogs perform
      ground-level operations such as video surveillance, material
      transport and obstacle clearance.

   *  Low latency tasks: In collaborative multi-agent systems, control
      signal transmission tasks impose significantly more stringent
      latency requirements than routine model training data transfer.
      For example, the home robot remotely sends an alarm message to the
      end user.

   *  High reliability tasks: Smart factory scenarios require critical
      reliability of agent task execution and fault-tolerant operation
      stability.

   These categories of use cases (may be further extended) demonstrate
   the collaboration among agents spanning multiple distinct domains to
   achieve end-to-end task completion.  The embodied agents (such as the
   robots and unmanned aerial vehicles) interacting with physical
   environments through embodied interfaces, while virtual agents (such
   as the various software applications and personal assistant)
   providing complementary capabilities, has demonstrated the advantages
   of collaboratively completing complex tasks in various scenarios.

3.  Necessity

3.1.  Task Complexity

   As task complexity increases, heterogeneous agents require multiple
   interaction rounds, precise planning, ordered execution, and
   efficient context sharing mechanisms to enhance resolution quality
   and robustness.



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3.2.  Resource Optimization

   Through task coordination and resource consumption monitoring, the
   multi-agent systems are able to support dynamic allocation of for
   example, the computing, storage and bandwidth resources to optimize
   the resource utilization efficiency.

3.3.  Quality of Service

   Task coordination may dynamically prioritize resources allocations
   based on for example, task priorities, agent expertise and Quality of
   Service requirements.  This ensures timeliness and accuracy of
   critical tasks, reduces service response latency, and maintains
   output stability and reliability.

3.4.  Dynamic Adjustment

   The agents may update or adjust the task during task execution phase
   based on end user's inputs or contextual updates to better respond to
   the final task requirements.

4.  Protocol Requirements

4.1.  Task Description

   Precise task descriptions or task templates are REQUIRED to ensure
   all agents maintain a consistent understanding of the objectives,
   operational constraints and criteria.

   A well-defined task description:

   *  Reduces ambiguity: Minimizes misinterpretations and conflicting
      actions among agents.

   *  Enables verifiability: Translates abstract goals into executable
      and measurable plans.

   *  Improves robustness: Ensures collaboration remains coherent and
      efficient under dynamic conditions.

   Task descriptions assigned to different agents MUST follow the
   minimization principle, i.e., agents SHOULD receive only the minimal,
   contextually necessary information required to fulfill their tasks to
   prevent unauthorized access of sensitive information.







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4.2.  Task State

   Upon receiving a task, the Coordinator Agent may decompose it into
   multiple tasks and delegate them to different Execution Agents via
   dynamic capability discovery mechanism.  The AI Agent protocol design
   should support comprehensive state descriptions throughout the task
   execution lifecycle.  For example, the definition of potential task
   states, (such as task submitted, running, suspended (awaiting
   external input or output from other agents), completed, canceled,
   rejected and failed) and the coordination operations (such as state
   queries, retrieval and push of intermediate results).

   Based on the length of time to complete the tasks, the task can be
   categorized into Short-term tasks that require a single request-
   response interactions and Long-term tasks that may require multi-
   round interactions or extended waiting periods.  The Coordinator
   Agent may dynamically adjust the target of the task according to the
   intermediate results of the Execution Agents and the context
   information.  The AI Agent protocol design should support long-term
   and short-term tasks coordination.

4.3.  Communication Mechanism

   When multiple agents participate in coordinated tasks, they may need
   to maintain common context sharing, or subscriptions to identical
   message content.  Different communication mechanisms, such as
   request/response and broadcast may need to be supported in the AI
   Agent protocol.  In the moving large artifact task in intelligent
   factory case, the multiple robots responsible for the transportation
   needs to obtain consistent route and destination data to ensure
   operational coherence.

   The applicable communication mechanisms may be vary for different
   agent communication structures, which directly affects the message
   delivery efficiency, implementation feasibility and system
   complexity.  The typical agent communication structures
   [Multi-Agents] including:

   *  Layered communication: Agents at different layers in the
      hierarchical structure have different roles or capabilities.
      Communication occurs either horizontally (intra-layer) or
      vertically (adjacent layers only).

   *  Decentralized communication: Multiple agents form a peer-to-peer
      network permitting direct communications between any two agents.






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   *  Centralized communication: Central agents coordinate the
      communications, with the other agents primarily interact through
      the central node.

   *  Shared message pool [MetaGPT]: Agents publish structured messages
      to the pool and subscribe to the message types matching their
      profiles.

   The task collaboration mode may also affect the communication
   mechanism between agents:

   *  In the primary/secondary mode, a Coordinator Agent decomposes a
      task into multiple tasks and distributes them to Execution Agents
      for processing.  The Execution Agents return their execution
      results to the Coordinator Agent, which aggregates the results and
      delivers them to the end user.

   *  In the peer negotiation mode, a Coordinator Agent distributes the
      task of the end user to the Execution Agents, which independently
      execute their assigned tasks.  The Execution Agents return their
      results directly to the end user via the Coordinator Agent,
      without requiring further processing by the Coordinator Agent.

   *  In subscription mode, a Coordinator Agent may delegate
      subscription-based tasks to Execution Agents.  For example, a task
      including "book a ticket to Beijing one week before the New Year
      holiday" will be assigned by the Coordinator Agent which will
      perform the task at the specified time to the Execution Agents.

   Different tasks may require long and short connections, the AI Agent
   protocol should be able to provide mechanisms beyond simple request/
   response, including the complex interaction modes for example message
   multicast, publication/subscription (PUB/SUB), asynchronous
   notifications.

   The AI Agent protocol design MUST consider support for relay nodes to
   facilitate task message forwarding.  Relay nodes SHOULD prioritize
   message scheduling and forwarding based on task requirements to
   ensure efficient agent collaboration and meet transmission QoS
   objectives.

   For example, relay nodes MAY implement the following priority
   hierarchy (from highest to lowest):

   *  Control signaling transmission tasks

   *  Media stream transmission tasks




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   *  Training data stream transmission tasks

   This prioritization scheme ensures that critical messages receive
   preferential treatment during congestion or resource contention
   scenarios.

4.4.  Context Sharing

   When delegating tasks to Execution Agents, the Coordinator Agent may
   include task-relevant contextual about the contact information of the
   end user, the task itself, the historical preference information
   known by the Coordinator Agent, and other necessary conversation
   data, to facilitate the task execution.  For example, in trip
   planning case, this may encompass historically booked flight/hotel
   preferences or dynamically perceived context like recent user dialog.
   The AI Agent protocol design should consequently support context
   sharing mechanisms through standardized definitions of context types,
   length constraints, and encoding formats to enhance the effectiveness
   of task execution.

   The context sharing MAY have an impact on privacy of the user, it is
   necessary to consider the limitations of the scope of context
   sharing, especially for the sensitive information e.g. name, age,
   address of the user.

4.5.  Exception Handling

   Exception handling constitutes a critical mechanism for multi-agent
   collaborative task execution.  If an execution agent cannot complete
   an assigned task due to lack of skills or overloaded, the failure in
   task execution may lead to such as releasing the connections.

5.  Existing Protocol Analysis

   Task-oriented coordination is compatible with multiple types of
   existing protocols, such as TCP, HTTP and etc.

   Transmission Control Protocol (TCP) is to provide reliable, orderly,
   connection-oriented data transmission services for end-to-end
   communication.  In the task-oriented coordination scenarios, some TCP
   capabilities can be directly reused (e.g. retransmission mechanism,
   congest control and etc.).

   The Hypertext Transfer Protocol (HTTP) is the application layer
   protocol for distributed, collaborative, hypermedia information
   systems.  Some HTTP features are also applicable to task-oriented
   coordination.




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   However, the task-oriented coordination needs to support finer-
   grained access control and context information anonymization
   mechanisms, which may need the enhancements on the protocol, defining
   the task-oriented coordination mechanism at the session layer has
   some advantages.

6.  Conclusions

   Task-oriented coordination constitutes a critical function for multi-
   agent collaboration.  This document discusses the necessity of
   introducing task-oriented coordination to address complex tasks,
   optimize resource utilization, and guarantee service quality.
   Consequently, it analyzes the requirements imposed by task-oriented
   coordination on AI Agent protocol design, specifically concerning
   task descriptions, task states, communication mechanisms, context
   sharing, and exception handling.

7.  IANA Considerations

   This memo includes no request to IANA.

8.  Security Considerations

   When designing the task-oriented coordination for AI agents
   communication, privacy should always be considered.

9.  References

9.1.  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>.

9.2.  Informative References

   [Multi-Agents]
              Guo, T., Chen, X., Wang, Y., Chang, R.i., Pei, S., Chawla,
              N.V., Wiest, O., and X. Zhang, "Large Language Model based
              Multi-Agents: A Survey of Progress and Challenges", 2024.

   [MetaGPT]  Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y.,
              Zhang, C., Wang, J., Wang, Z., Yau, S., Lin, Z., Zhou, L.,
              Ran, C., Xiao, L., Wu, C., and J. Schmidhuber, "MetaGPT:
              Meta Programming for A Multi-Agent Collaborative
              Framework", 2023.




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Authors' Addresses

   Yong Cui
   Tsinghua University
   Beijing, 100084
   China
   Email: cuiyong@tsinghua.edu.cn
   URI:   http://www.cuiyong.net/


   Chenguang Du
   Zhongguancun Laboratory
   Beijing, 100094
   China
   Email: ducg@zgclab.edu.cn




































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