Working Group Z. Chang Internet-Draft S. Peng Intended status: Standards Track Huawei Technologies Expires: 14 August 2026 10 February 2026 Agent Context Interaction Optimizations draft-chang-agent-context-interaction-00 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 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 14 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 Chang & Peng Expires 14 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 . . . . . . . . . . . . . . . . . . . . . . . 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. Chang & Peng Expires 14 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. 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. Chang & Peng Expires 14 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 14 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 be Chang & Peng Expires 14 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 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. Chang & Peng Expires 14 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 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. Chang & Peng Expires 14 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 own Chang & Peng Expires 14 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 item Chang & Peng Expires 14 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 +------+ +------+ TaskContext Chang & Peng Expires 14 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 14 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, . [RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, May 2017, . Appendix A. Contributor Addresses Chang & Peng Expires 14 August 2026 [Page 12] Internet-Draft PCECC-SR February 2026 Yiyang Shao Huawei India EMail: shaoyiyang@huawei.com Tao Liu Huawei China Email: liutao417@huawei.com Jinyang Li Huawei India 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 Chang & Peng Expires 14 August 2026 [Page 13]