



Working Group                                                   Z. Chang
Internet-Draft                                                   S. Peng
Intended status: Standards Track                     Huawei Technologies
Expires: 16 August 2026                                 12 February 2026


                Agent Context Interaction Optimizations
                draft-chang-agent-context-interaction-01

Abstract

   The context distribution is important in a multi-agent system, which
   will impact the execution latency, token consumption, and task
   completion success rate, especially in a complex and multi-round
   workflow.

   This document specifies the scenarios and procedures of agent context
   distribution as well as the corresponding optimization during the
   procedures in order to provide precise control of the context
   distribution among the multiple agents.

Status of This Memo

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   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 16 August 2026.

Copyright Notice

   Copyright (c) 2026 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
   and restrictions with respect to this document.  Code Components



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   extracted from this document must include Revised BSD License text as
   described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Conventions and Terminology . . . . . . . . . . . . . . .   3
     2.2.  Requirements Language . . . . . . . . . . . . . . . . . .   4
   3.  Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . .   4
   4.  Agent Context Interaction Optimizations . . . . . . . . . . .   6
     4.1.  Overview of Agent Context Interaction Optimizations . . .   6
     4.2.  Context Isolation Strategy  . . . . . . . . . . . . . . .   7
   5.  JSON Specifications . . . . . . . . . . . . . . . . . . . . .   7
     5.1.  TaskContext Schema  . . . . . . . . . . . . . . . . . . .   7
     5.2.  AgentContext Schema . . . . . . . . . . . . . . . . . . .   8
     5.3.  Interaction Process of Agent Context Interaction
           Optimizations . . . . . . . . . . . . . . . . . . . . . .  10
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  11
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  12
   8.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  12
   9.  Normative References  . . . . . . . . . . . . . . . . . . . .  12
   Appendix A.  Contributor Addresses  . . . . . . . . . . . . . . .  12
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  13

1.  Introduction

   In a multi-agent system, contexts such as agents' capabilities,
   metadata, and intermediate results are exchanged among agents through
   an Agent2Agent protocol to support task execution.

   At present, agent context interaction commonly occurs in two
   scenarios: interaction among independent agents and interaction among
   collaborative agents.  In both scenarios, context information is
   typically distributed in a raw or unfiltered manner, without task-
   aware or relevance-based processing.  As a result, large volumes of
   redundant or irrelevant context are propagated across agents, leading
   to excessive token consumption.

   Furthermore, existing agent interaction protocols generally lack
   explicit task management mechanisms, such as task state tracking and
   progress awareness, for complex tasks that require multi-turn
   interactions and the coordinated invocation of multiple agents.  In
   the absence of structured task context, the LLM’s attention may drift
   across interaction rounds, which can significantly increase execution
   latency and reduce overall task success rates.




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   In this document, we assume a task-oriented multi-agent control model
   in which a designated Master Agent is responsible for global task
   orchestration, while other agents are invoked on demand to execute
   specific subtasks.  The Master Agent receives the user request,
   constructs the overall task, decomposes it into subtasks, and
   coordinates their execution by selectively invoking appropriate
   agents.

   Invoked agents operate under the control of the Master Agent and
   focus exclusively on the execution of assigned subtasks.  They do not
   maintain a global view of the task and do not directly interact with
   end users or other invoked agents unless explicitly mediated by the
   Master Agent.  This master–invoked agent architecture establishes a
   clear separation between global task management and localized task
   execution.

   Efficient context distribution is a critical capability in such a
   master–invoked multi-agent system and is largely determined by the
   design of the agent context interaction protocol.  The protocol
   directly controls how much context is exchanged, how it is
   structured, and how it is propagated across agents.  As a result, it
   has a direct and measurable impact on execution latency, token
   consumption, and task completion success rate, particularly in
   complex and multi-round agent workflows.

   This document specifies the scenarios and procedures for agent
   context distribution and introduces corresponding optimization
   mechanisms applied during these procedures.  The goal is to provide
   precise and task-aware control over context dissemination among
   multiple agents, thereby improving execution efficiency, reducing
   unnecessary token usage, and enhancing task completion success rates.

2.  Terminology

2.1.  Conventions and Terminology

   TaskContext: A structured state object that is created and maintained
   exclusively by the Master Agent throughout the lifecycle of a complex
   task.  It provides a persistent and machine-interpretable
   representation of task progress, enabling the Master agent to manage
   attention across multiple subtasks and prevent unintended context
   drift during multi-step execution.









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   AgentContext: The execution state that is specific to an individual
   agent within a multi-agent workflow.  It is strictly isolated between
   subtasks; during each subtask invocation, only the AgentContext
   instance corresponding to the target agent is delivered.  Upon
   completion, the corresponding agent returns exclusively its own
   updated AgentContext

   SynchronousContextInteraction: A context sharing mode in which an
   agent MUST wait for the completion of the current interaction round,
   including context exchange and result feedback, before proceeding to
   the next task or interaction.  In this mode, context distribution and
   task execution are strictly ordered, ensuring deterministic task
   progression and explicit dependency resolution among agents.

   AsynchronousContextInteraction: A context sharing mode in which an
   agent MAY exchange contexts with multiple agents concurrently without
   waiting for the completion of other ongoing interactions.  This mode
   allows parallel context distribution and task execution, enabling
   higher system throughput and reduced overall execution latency in
   multi-agent collaborative workflows.

   Master Agent: The agent responsible for receiving user requests,
   maintaining the global TaskContext, decomposing complex tasks into
   subtasks, invoking other agents, and evaluating subtask execution
   results.

   Invoked Agent: An agent that is invoked by the Master Agent to
   execute a specific subtask.  An Invoked Agent operates on an isolated
   AgentContext and returns only its updated execution state to the
   Master Agent upon completion.

2.2.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

3.  Scenarios

   There are two main scenarios when comes to the context distribution
   among agents, that is, (1) Context Distribution among Independent
   Agents and (2) Context Distribution among Collaborative Agents.







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                       +-------------+
                       |    Master   |
                       |    Agent    |
                     / +-------------+ \
                 1 /        1 |        1 \
                 / 2          |2           \2
               /              |              \
       +------------+  +-------------+  +------------+
       |   Agent 1  |  |   Agent 2   |  |   Agent 3  |
       +------------+  +-------------+  +------------+

      (1) Context Distribution among Independent Agents

                       +-------------+
                       |    Master   |
                       |    Agent    |
                     / +-------------+ \
                 1 /        3 |        5 \
                 / 2          |4           \6
               /              |              \
       +------------+  +-------------+  +------------+
       |   Agent 1  |  |   Agent 2   |  |   Agent 3  |
       +------------+  +-------------+  +------------+

      (2) Context Distribution among Collaborative Agents



               Figure 1: Agent Context Distribution Scenarios

   In the first scenario, i.e. Context Distribution among Independent
   Agents, the agents in the multi-agent system are independent.  The
   Master Agent receives the request, creates a main task and breaks
   down into several sub-tasks, and distribute those sub-tasks to
   correponding agents.  The discover of the correponding agents is out
   of the scope of this draft.  This could be for a tour-plan task,
   which could involve the agents of hotel-booking, flight-booking, and
   weather-checking.  When the Master Agent receives the tour-plan task,
   it will break it down into sub-tasks and distribute context to each
   agent to accomplish the task.

   In the second scenario, i.e. Context Distribution among Collaborative
   Agents, the agents in the multi-agent system are collaborative, that
   is, the input of the latter agent in a workflow will depend upon the
   former agent.  The Master Agent receives the request, creates a main
   task and breaks down into several sub-tasks.  It will distribute the
   sub-tasks to correponding agents in an order.  The discover of the
   correponding agents is out of the scope of this draft.  This could be



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   for a see-a-doctor task, which could involve the agents of disease-
   diagnosis, prescription, and buy-medicine.  When the Master Agent
   receives the see-a-doctor task, it will break it down into sub-tasks
   and distribute context to the first agent, and then depending upon
   the output of the first agent, the second accomplishes its
   corresponding tasks, and then the next agent follows the same
   procedure.

4.  Agent Context Interaction Optimizations

4.1.  Overview of Agent Context Interaction Optimizations



        +--------------------------------------------------------------+
        |                          Data Layer                          |
        +--------------------------------------------------------------+
        |                                                              |
        | TaskContext  AgentContext  DeviceContext  EnvironmentContext |
        |                                                              |
        +--------------------------------------------------------------+

        +--------------------------------------------------------------+
        |                       Operation Layer                        |
        +--------------------------------------------------------------+
        |                                                              |
        | SynchronousContextInteraction  AsynchronousContextInteraction|
        |                                                              |
        +--------------------------------------------------------------+

        +--------------------------------------------------------------+
        |                      Transport Layer                         |
        +--------------------------------------------------------------+
        |                                                              |
        |    STDIO     StreamablrHTTP     WebSocket    SoftBus         |
        |                                                              |
        +--------------------------------------------------------------+




  Figure 2: Architecture of the Agent Context Interaction Optimizations

   We design a structured context interaction format to optimize context
   exchange in multi-agent systems across several key dimensions.






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   Agent context interaction optimizations are designed across three
   layers: the data layer, the operation layer, and the transport layer.
   At the data layer, we introduce structured context formats, including
   the TaskContext and AgentContext, to precisely capture and represent
   task and agent states.  The detailed design of DeviceContext and
   EnvironmentContext will be done later.  SynchronousContextInteraction
   and AsynchronousContextInteraction are desined for both scenarios
   mentioned in Section 3, respecitively.  The corresponding transport
   protocols are designed to support different sceanrios.

   A context isolation strategy is introduced to ensure that, during
   collaborative task execution, each agent receives only the context
   strictly relevant to its assigned task.  Relevant context is
   precisely distributed to the corresponding agent in a well-structured
   manner, which reduces interference caused by unnecessary or unrelated
   contextual information.

   A fine-grained task management mechanism is defined for both the
   Master Agent and invoked agents.  Clear task progress monitoring and
   handling procedures are specified, which mitigate attention drift
   within the model and improve overall task completion success rates.

   An efficient two-layer task evaluation mechanism is further
   established.  The Master Agent evaluates the outputs of the invoked-
   agent based on specific fields in the structured context, avoiding
   the need to process or transmit full-context outputs and thereby
   reducing excessive token consumption.

   Support for context offloading is also provided.  Both the Master
   agent and invoked agents are allowed to store or offload full context
   data to external storage systems, while only structured task
   descriptions and references to the full context are exchanged.  This
   approach significantly reduces the overall token consumption of the
   multi-agent system.

4.2.  Context Isolation Strategy

5.  JSON Specifications

5.1.  TaskContext Schema

   The TaskContext schema defines a structured state object that is
   created and maintained exclusively by the Master Agent throughout the
   lifecycle of a complex task.  Its purpose is to provide a persistent
   and machine-interpretable representation of task progress, enabling
   the Master Agent to manage attention across multiple subtasks and
   prevent unintended context drift during multi-step execution.




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    "TaskContext": {
       "TaskID": "",
       "UserQuery": "",
       "TaskName": "",
       "TaskDescription": "",
       "GoalStatus": [
           {"Goal": "", "Status": ""},
           {"Goal": "", "Status": ""},
           {"Goal": "", "Status": ""}
       ],
       "OverallStatus": "",
       "StartTime": "",
       "EndTime": "",
       "Optional": ["StartTime", "EndTime"]
       },



                    Figure 3: JSON Format - TaskContext

   The TaskContext schema includes fundamental identification and intent
   fields such as TaskID, UserQuery, TaskName, and TaskDescription,
   which collectively capture the origin and semantic scope of the task.
   The GoalStatus array records the progress of individual goals using
   explicit {Goal, Status} tuples, allowing the Master Agent to track
   partial completion and reason over intermediate milestones.  An
   aggregated OverallStatus field reflects the global execution state
   derived from these goals.

   Temporal attributes StartTime and EndTime provide optional lifecycle
   metadata for auditing and scheduling purposes.  These fields are
   listed in the Optional array to indicate that their inclusion is not
   mandatory for functional correctness and may be omitted to reduce
   token overhead when time information is not required.

   By maintaining this structured context, the Master Agent can
   explicitly steer the model’s focus toward active objectives, mitigate
   attention fragmentation across turns, and ensure consistent task
   execution semantics in long-running multi-agent workflows.

5.2.  AgentContext Schema

   The AgentContext schema represents the execution state that is
   specific to an individual agent within a multi-agent workflow.  The
   messages of different agents are strictly isolated between subtasks;
   during each subtask invocation, only the AgentContext instance
   corresponding to the target agent is delivered.  Likewise, upon
   completion, the corresponding agent returns exclusively its own



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   updated AgentContext to the Master Agent, without access to contexts
   belonging to other agents.  This design enforces information
   isolation and prevents unintended cross-agent context contamination.

   "AgentContext": {
           "AgentID": "",
           "AgentName": "",
           "SubTaskID": "",
           "SubTaskName": "",
           "Dependencies": [],
           "Context/ContextURI": "",
           "todoItems": [
               {"itemId": "", "description": ""},
               {"itemId": "", "description": ""},
               {"itemId": "", "description": ""}
           ],
           "ItemstateUpdates": [
               {"itemId": "", "state": 0},
               {"itemId": "", "state": 1},
               {"itemId": "", "state": 0}
           ],
           "KeyInformation": [
               {"itemId": "", "outputabstract": ""},
               {"itemId": "", "outputabstract": ""},
               {"itemId": "", "outputabstract": ""}
           ],
           "LastUpdated": "",
           "Optional": ["LastUpdated"]
       },


                    Figure 4: JSON Format - AgentContext

   The AgentContext schema contains identification fields including
   AgentID, AgentName, SubTaskID, and SubTaskName, which bind the
   context to a concrete subtask instance.  The Dependencies array
   expresses prerequisite relations among subtasks, enabling the Master
   Agent to reason about execution ordering and blocking conditions.

   The field Context/ContextURI specifies the address of external or
   long-term context required for each agent to perform its task, such
   as retrieved documents, knowledge bases, or cached intermediate
   results.  This indirection avoids embedding large artifacts directly
   in the messages and supports scalable context management.

   Task execution is modelled through three coordinated structures.  The
   todoItems array enumerates actionable steps assigned to each agent.
   The ItemstateUpdates array records the completion state of each item



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   using a binary code where 0 denotes not completed and 1 denotes
   completed.  The KeyInformation array provides concise output
   abstracts generated by the agent for each item, capturing the
   semantic evidence necessary for result validation.

   After receiving the updated AgentContext, the Master Agent evaluates
   subtask completion in a two-phase manner.  It first inspects
   ItemstateUpdates for a preliminary decision; items marked as 0 are
   considered unfinished and bypass further assessment.  For items
   marked as 1, the Master Agent performs a second-stage verification
   based on the corresponding KeyInformation, ensuring that the produced
   outputs satisfy the intent of the subtask.

   The optional field LastUpdated records the timestamp of the most
   recent modification and is listed in the Optional array to indicate
   that its inclusion is not required for functional correctness.

5.3.  Interaction Process of Agent Context Interaction Optimizations




+------+   User Query+   +------+              +------+         +------+
|      |  Sysytem Prompt |      |              |      |         |      |
|      <-----------------+      |              |      |         |      |
|      |   TaskContext   |      |              |      |         |      |
|      +----------------->      |              |      |         |      |
|      |                 |      |              |      |         |      |
|      |  TaskContext+   |      |              |      |         |      |
|      |  System Prompt  |      |              |      |         |      |
|      <-----------------+      |              |      |         |      |
|Master|   AgentContext  | ACI  |     Send     | ACI  |         |Server|
|Agent +----------------->Client| AgentContext |Server|         |Agent |
|      |                 |      +-------------->      |todoItems|      |
|      |                 |      |              |      +--------->      |
|      |                 |      |    return    |      | Results |      |
|      |ItemStateUpdates+|      | AgentContext |      <---------+      |
|      |KeyInformation   |      <--------------+      |         |      |
|      <-----------------+      |              |      |         |      |
|      |                 |      |              |      |         |      |
|      |     Evaluation  |      |              |      |         |      |
|      |      Results    |      |              |      |         |      |
|      +----------------->      |              |      |         |      |
|      |                 |      +---+          |      |         |      |
|      |                 |      |   |          |      |         |      |
|      |                 |      <---+          |      |         |      |
+------+                 +------+ Updated      +------+         +------+
                                  TaskContext



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   Figure 5: Interaction Flowchart of the Agent Context Interaction
                            Optimizations

   Figure 5 illustrates the interaction flow of the proposed Agent
   Context Interaction (ACI) optimizations in a multi-agent system.  The
   workflow begins when the Master Agent receives a user query together
   with the system prompt, forming an initial TaskContext.  The
   TaskContext is exchanged with the ACI Client, which acts as the
   interface for context transmission.

   It is worth mentioning that the ACI Client and ACI Server are
   conceptual entities utilized to illustrate the logical connection
   between the Master Agent and the invoked agents.  In practice, these
   components can be realized by an Agent2Agent (A2A) client and an A2A
   server, without introducing additional architectural constraints.

   Based on the task decomposition, the Master Agent generates an
   isolated AgentContext corresponding to a specific sub-task and sends
   it through the ACI Client and ACI Server to the target Server Agent.
   Only task-relevant information, including todoItems and the
   associated context reference, is delivered to the Server Agent,
   ensuring precise and minimal context distribution.

   After executing the assigned sub-task, the Server Agent returns an
   updated AgentContext containing ItemStateUpdates and KeyInformation.
   These updates reflect task completion status and summarized outputs
   rather than full execution traces.  The Master Agent then evaluates
   the returned information to assess task progress and correctness.

   Upon evaluation, the Master Agent updates the global TaskContext
   accordingly, enabling subsequent task scheduling or further agent
   interactions.  This flow demonstrates how structured context
   isolation, incremental state updates, and layered evaluation
   collectively reduce token consumption, control attention drift, and
   improve overall execution efficiency in multi-agent collaboration.

6.  Security Considerations

   Agent Context Interaction Optimazation defines structured context
   exchange mechanisms among cooperating agents.  While we do not
   introduce a new transport protocol, improper handling of context data
   may lead to security and privacy risks.









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   AgentContext messages may contain sensitive information, including
   user queries, intermediate reasoning artifacts, task metadata, and
   execution results.  Implementations MUST ensure confidentiality and
   integrity of AgentContext messages by relying on secure underlying
   transport mechanisms, such as TLS-protected channels, and appropriate
   authentication and access control between agents.

   The protocol promotes context isolation by design, where each agent
   receives only the context strictly required for its assigned task.
   Failure to enforce this isolation may result in unintended
   information disclosure or cross-task data leakage.

   This document does not define agent identity formats or trust
   establishment procedures.  Deployments are expected to rely on out-
   of-band mechanisms for agent authentication, authorization, and trust
   management.

7.  IANA Considerations

   This document does not require any immediate actions by IANA.  Future
   extensions of the protocol may define registries or code points that
   require IANA assignment.

8.  Acknowledgments

   We would like to thank Yiyang Shao, Jinyang Li and Tao Liu for useful
   discussion and ideas.

9.  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/info/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/info/rfc8174>.

Appendix A.  Contributor Addresses











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   Yiyang Shao
   Huawei
   China

   EMail: shaoyiyang@huawei.com

   Tao Liu
   Huawei
   China

   Email: liutao417@huawei.com

   Jinyang Li
   Huawei
   China

   EMail: lijinyang9@huawei.com



                                  Figure 6

Authors' Addresses

   Zeze Chang
   Huawei Technologies
   No. 3 Shangdi Information Rd.
   Beijing
   100085
   China
   Email: changzeze@huawei.com


   Shuping Peng
   Huawei Technologies
   Huawei Bld., No.156 Beiqing Rd.
   Beijing
   100095
   China
   Email: pengshuping@huawei.com











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