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Agent Context Interaction Optimizations
draft-chang-agentproto-context-interaction-00

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Document	Type	Active Internet-Draft (individual)
	Authors	Zeze Chang , Shuping Peng
	Last updated	2026-10-25
	Replaces	draft-chang-agent-context-interaction
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draft-chang-agentproto-context-interaction-00

Agentproto Z. Chang
Internet-Draft S. Peng
Intended status: Standards Track Huawei Technologies
Expires: 28 April 2027 25 October 2026

Agent Context Interaction Optimizations
draft-chang-agentproto-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 28 April 2027.

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 . . . . . . . . . . . . . . . . . . . . . . 13
6. Security Considerations . . . . . . . . . . . . . . . . . . . 15
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 16
8. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 16
9. Normative References . . . . . . . . . . . . . . . . . . . . 16
Appendix A. Contributor Addresses . . . . . . . . . . . . . . . 16
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 17

1. Introduction

In a multi-agent system, contexts such as agents' capabilities,
metadata, and intermediate results are exchanged among agents through
an Agent2Agent (A2A)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
Server Agents.

Invoked Server 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 subtasks, and distribute those subtasks 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 subtasks 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 subtasks. It will distribute the
subtasks 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 subtasks
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 StreamableHTTP 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 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

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.

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": [],
"ContextItems": [
{
"ContextID": "",
"ContextType": "document|graph|video|image|audio|code|table|dataset|text|other",
"MediaType": "",
"ContextURI": "",
"Description": "",
"Priority": "must_preserve|compressible|reference_only|discardable"
}
...
],
"CompressionPolicy": {
"Strategy": "target_ratio",
"TargetRatio": 0.3,
"LossTolerance": "low",
"MustPreserveFields": [
"KeyInformation",
"Dependencies",
"ContextItems[].Description"
]
},
"todoItems": [
{"itemID": "", "description": ""},
{"itemID": "", "description": ""},
{"itemID": "", "description": ""}
],
"ItemStateUpdates": [
{"itemID": "", "state": 0},
{"itemID": "", "state": 1},
{"itemID": "", "state": 0}
],
"KeyInformation": [
{"itemID": "", "outputabstract": ""},
{"itemID": "", "outputabstract": ""},
{"itemID": "", "outputabstract": ""}
],
"EvaluationPolicy": {
"SuccessCriteria": [

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{
"CriterionID": "",
"Description": "",
"Required": true
}
...
],
"ConfidenceThreshold": 0.8
},
"EvaluationResult": {
"Status": "",
"Confidence": 0,
"CriterionResults": [
{
"CriterionID": "",
"Status": "",
"EvidenceRefs": [],
"TraceRefs": []
}
...
],
"FailureReason": ""
},
"LastUpdated": "",
"Optional": [
"LastUpdated",
"CompressionPolicy"
]
}

Figure 4: JSON Format - AgentContext

The AgentContext schema contains identification fields including
AgentID, AgentName, SubTaskID, and SubTaskName, which bind the
context to a concrete agent and subtask instance. The Dependencies
array expresses prerequisite relations among agents or subtasks,
enabling the Master Agent to determine execution ordering and to
identify upstream outputs required by the current subtask.

The ContextItems array provides a structured representation of the
contextual information required by the target agent. Instead of
representing the context through a single ContextURI, the context can
be divided into multiple independently described context items. Each
item is identified by ContextID and includes ContextType, MediaType,
ContextURI, Description, and Priority. ContextType provides a
semantic classification of the context, such as document, graph,
video, image, audio, code, table, dataset, or text, while MediaType

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further specifies the concrete representation format. Together,
ContextType and MediaType enable the receiving agent to select an
appropriate tool or processing method for different types of context,
reducing unnecessary processing attempts and associated resource
consumption. They also allow heterogeneous and multimodal context
resources to be represented within a single AgentContext.

ContextURI identifies the location from which the corresponding
context item can be retrieved. Description provides a concise
semantic description of the content referenced by ContextURI,
allowing the receiving agent to understand what information the
referenced resource contains before accessing it. For example, a
Description may indicate that a ContextURI refers to a quarterly
sales report, a source-code repository, or a specific segment of a
video. Description therefore complements ContextURI by providing
human- and agent-readable semantic information about the referenced
context.

The Priority field specifies how an individual ContextItem should be
treated when the receiving Server Agent performs local context
compression. A value of must_preserve indicates that the item should
be retained without semantic compression; compressible allows
semantic reduction while preserving task-relevant information;
reference_only allows the content to be omitted while retaining its
reference and description; and discardable permits the item to be
removed when necessary. Priority therefore defines the permitted
reduction level of each ContextItem during the receiving Server
Agent's local context processing.

The CompressionPolicy specifies the overall compression strategy that
the receiving agent should apply during local processing of the
received AgentContext. Strategy specifies the compression method,
TargetRatio defines the intended overall compression ratio,
LossTolerance specifies the acceptable degree of semantic information
loss, and MustPreserveFields identifies fields that should remain
available after compression. TargetRatio is treated as a target
rather than a strict requirement when preservation constraints
prevent further reduction.

CompressionPolicy and ContextItems.Priority operate at different
levels. CompressionPolicy defines the overall compression objective
for an AgentContext, whereas Priority constrains how each individual
context item may be reduced. Item-level preservation constraints
take precedence over the global compression target. Consequently, an
implementation may produce a compressed context whose actual size
exceeds TargetRatio when further compression would violate Priority
or MustPreserveFields constraints.

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Task execution is modelled through three coordinated structures. The
todoItems array enumerates actionable steps assigned to the agent.
The ItemStateUpdates array records the completion state of each item
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 task-
relevant information required by the Master Agent for subsequent
reasoning and result validation.

The EvaluationPolicy specifies the evaluation requirements associated
with the current subtask. SuccessCriteria defines one or more
conditions against which the produced result can be assessed. Each
criterion is identified by CriterionID and described by Description.
The Required field indicates whether satisfaction of the
corresponding criterion is mandatory for successful completion of the
subtask. ConfidenceThreshold specifies the minimum confidence
expected for an acceptable evaluation result.

The EvaluationResult records the structured evaluation produced for
the subtask. Status represents the overall evaluation status and
Confidence expresses the associated confidence value.
CriterionResults provides criterion-level evaluation results and
associates each result with the corresponding CriterionID.
EvidenceRefs may reference contextual evidence supporting the
evaluation, while TraceRefs may reference execution traces associated
with the result. FailureReason provides additional information when
the evaluation does not satisfy the defined policy.

EvaluationPolicy and EvaluationResult represent different phases of
the subtask lifecycle. EvaluationPolicy is delivered as part of the
AgentContext before execution and specifies the conditions under
which the subtask result is expected to be considered acceptable.
EvaluationResult is populated after execution and returned with the
updated AgentContext. The Master Agent may use the returned
EvaluationResult together with ItemStateUpdates and KeyInformation to
perform its own final assessment before updating TaskContext.

After receiving the updated AgentContext, the Master Agent therefore
evaluates subtask completion using both execution-state information
and semantic result information. ItemStateUpdates provides a
preliminary indication of whether the assigned work has been
completed, while KeyInformation and EvaluationResult provide the
information required for semantic validation against the original
task objective and evaluation requirements. The Master Agent retains
authority over the final task-level decision and over subsequent
updates to TaskContext.

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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

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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. The workflow begins when
the Master Agent receives the user query and system prompt,
constructs the TaskContext, and generates an AgentContext for the
selected subtask.

The ACI Client and ACI Server are conceptual entities representing
the logical communication path between the Master Agent and the
invoked Server Agent. In practice, they may be implemented using
existing agent communication mechanisms, such as an Agent2Agent (A2A)
client and server.

The Master Agent sends the AgentContext to the Server Agent through
the ACI Client and ACI Server. The ACI Server delivers ContextItems
together with todoItems to provide the contextual information and
execution requirements of the subtask. CompressionPolicy specifies
the local context compression strategy to be followed by the
receiving Server Agent, while EvaluationPolicy defines the criteria
and confidence requirements used to evaluate the subtask result.

After executing the subtask, the Server Agent returns the execution
results to the ACI Server. The updated AgentContext, including
ItemStateUpdates, KeyInformation, and EvaluationResult, is then
returned to the Master Agent. ItemStateUpdates represents execution
status, KeyInformation provides the task-relevant output, and
EvaluationResult records the Server Agent's evaluation against the
received EvaluationPolicy.

The Master Agent further evaluates the returned AgentContext and
produces its own Evaluation Outputs before updating the global
TaskContext. Based on the updated TaskContext, the Master Agent can
determine whether the current subtask is complete and schedule
subsequent agent interactions. This process enables structured
context delivery, controlled local context processing, and layered
task evaluation in multi-agent collaboration.

By explicitly specifying the context type, format, and content
reference, the receiving Server Agent can select appropriate tools
and processing methods, reducing ineffective attempts, repeated
parsing, and unnecessary token consumption.

By defining the compression strategy, target ratio, loss tolerance,
and fields that must be preserved, the protocol provides consistent
constraints for how the receiving Server Agent compresses shared
context, thereby reducing the risk of losing critical information.

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By specifying success criteria and confidence requirements when a
task is assigned, the receiving Server Agent can execute the task
against a consistent evaluation objective, while structured
evaluation results help the Master Agent determine whether the task
has been successfully completed.

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.

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.

The use of the ContextURI field introduces an indirection mechanism
for accessing external or offloaded context data. While this
approach reduces token consumption by avoiding the transmission of
large artifacts, it also introduces additional security
considerations related to pointer integrity and distributed state
management.

Implementations MUST ensure the integrity and authenticity of
ContextURI references. A malicious or tampered ContextURI could
redirect an invoked agent to unauthorized resources or cause the
retrieval of manipulated context data. Appropriate protections, such
as signed references, authenticated storage endpoints, or secure
capability-based access mechanisms, SHOULD be employed to ensure that
only trusted agents can resolve and access the referenced context.

The lifecycle management of ContextURI references SHOULD also be
carefully controlled. Storage systems that host offloaded context
data SHOULD enforce explicit expiration policies (e.g., time-to-live

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or TTL) to prevent long-term persistence of sensitive information.
Access to such storage endpoints MUST require appropriate
authentication and authorization to ensure that only the intended
invoked agents can retrieve the referenced context.

Deployments MUST further consider data residency and privacy
implications when ContextURI references point to storage systems
containing user-related information, such as UserQuery or
intermediate task artifacts. Operators SHOULD ensure that storage
locations comply with applicable data protection policies and that
sensitive context data is protected both in transit and at rest.

The Master Agent and the storage endpoint referenced by ContextURI
are required to operate within a trusted relationship. The Master
Agent MUST ensure that the storage service providing context data
enforces adequate security controls and maintains the integrity and
availability of the stored context. Failure to ensure this trust
relationship may result in context poisoning, data tampering, or
unauthorized disclosure of task-related information.

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, Tao Liu, James Kunle
Olorundare, Ujjwal Upadhyay, Zhuoran Ma, Muhammad Awais Jadoon and
Muntaser Syed 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

Chang & Peng Expires 28 April 2027 [Page 17]

Agent Context Interaction Optimizations draft-chang-agentproto-context-interaction-00

Agent Context Interaction Optimizations
draft-chang-agentproto-context-interaction-00