Working Group                                                   Z. ChangInternet-Draft                                                   S. PengIntended status: Standards Track                     Huawei TechnologiesExpires: 16 August 2026                                 12 February 2026                Agent Context Interaction Optimizations                draft-chang-agent-context-interaction-01Abstract   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   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/.   Internet-Drafts are draft documents valid for a maximum of six months   and may be updated, replaced, or obsoleted by other documents at any   time.  It is inappropriate to use Internet-Drafts as reference   material or to cite them other than as "work in progress."   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 ComponentsChang & Peng             Expires 16 August 2026                 [Page 1]Internet-Draft                  PCECC-SR                   February 2026   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  . . . . . . . . . . . . . . . . . . . . . . .  131.  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.Chang & Peng             Expires 16 August 2026                 [Page 2]Internet-Draft                  PCECC-SR                   February 2026   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.  Terminology2.1.  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.Chang & Peng             Expires 16 August 2026                 [Page 3]Internet-Draft                  PCECC-SR                   February 2026   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.Chang & Peng             Expires 16 August 2026                 [Page 4]Internet-Draft                  PCECC-SR                   February 2026                       +-------------+                       |    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 beChang & Peng             Expires 16 August 2026                 [Page 5]Internet-Draft                  PCECC-SR                   February 2026   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 Optimizations4.1.  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.Chang & Peng             Expires 16 August 2026                 [Page 6]Internet-Draft                  PCECC-SR                   February 2026   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 Strategy5.  JSON Specifications5.1.  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.Chang & Peng             Expires 16 August 2026                 [Page 7]Internet-Draft                  PCECC-SR                   February 2026    "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 ownChang & Peng             Expires 16 August 2026                 [Page 8]Internet-Draft                  PCECC-SR                   February 2026   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 itemChang & Peng             Expires 16 August 2026                 [Page 9]Internet-Draft                  PCECC-SR                   February 2026   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      +------+         +------+                                  TaskContextChang & Peng             Expires 16 August 2026                [Page 10]Internet-Draft                  PCECC-SR                   February 2026   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.Chang & Peng             Expires 16 August 2026                [Page 11]Internet-Draft                  PCECC-SR                   February 2026   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 AddressesChang & Peng             Expires 16 August 2026                [Page 12]Internet-Draft                  PCECC-SR                   February 2026   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 6Authors' 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.comChang & Peng             Expires 16 August 2026                [Page 13]