nmop Q. Sun Internet-Draft R. Luo Intended status: Standards Track Y. Fu Expires: 20 January 2027 C. Xie China Telecom 19 July 2026 The requirements of Agent Lifecycle Management draft-sun-nmop-agent-lifecycle-management-00 Abstract As agents evolve from auxiliary analysis toward autonomous decision- making and task execution, their operation involves multiple dynamic factors, including models, knowledges, metrics, tools, permissions, behaviors, and collaborative relationships. Traditional management methods are no longer sufficient to ensure their secure and stable operation. This draft proposes an agent lifecycle management requirement covering registration and publication, deployment and operation, continuous operations, capability evolution, deactivation, and retirement. It analyzes key management requirements related to assets, capabilities, knowledge, tools, identity and authorization, operational evaluation, auditing, and version evolution and retirement, providing the unified management, continuous governance, and large-scale deployment of 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 20 January 2027. Copyright Notice Copyright (c) 2026 IETF Trust and the persons identified as the document authors. All rights reserved. Sun, et al. Expires 20 January 2027 [Page 1] Internet-Draft AgentOps July 2026 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 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 . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1. Scope . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2. Requirements Language . . . . . . . . . . . . . . . . . . 6 1.3. Definition and Terminology . . . . . . . . . . . . . . . 6 2. Why the Agent Lifecycle Management is important? . . . . . . 6 2.1. Network Operation Agents Are Evolving from Decision Support to Autonomous Execution, Increasing Operational Risks . . 6 2.2. Network Operation Agents Continuously Evolve, Making One-Time Management Approaches Insufficient . . . . . . . 7 2.3. Network Operation Agents Depend on Multiple Capability factors and Require Unified Management . . . . . . . . . 7 2.4. Large-Scale Deployment of Network Operation Agents Requires a Unified Operational Governance Mechanism . . . . . . . 8 2.5. Network Operation Agents Should Continuously Create the Value of Operation . . . . . . . . . . . . . . . . . . . 8 3. Requirements for Agent Lifecycle Management . . . . . . . . . 8 3.1. Agent Asset and Accountability Management . . . . . . . . 9 3.2. Agent Capability Composition and Configuration Management . . . . . . . . . . . . . . . . . . . . . . . 9 3.3. Network Performance Parameters, Knowledge, Ontology, and Memory Management . . . . . . . . . . . . . . . . . . . . 10 3.4. Tool, Skill, and Collaboration Relationship Management . 11 3.5. Identity, Authorization, and Trust Relationship Management . . . . . . . . . . . . . . . . . . . . . . . 12 3.6. Quality, Effectiveness, and Performance Management . . . 13 3.7. Runtime Behavior, Reliability, and Cost Management . . . 13 3.8. Security, Risk, Compliance, and Audit Management . . . . 14 3.9. Version Change, Evolution, and Retirement Management . . 15 4. Security Considerations . . . . . . . . . . . . . . . . . . . 16 4.1. Identity and Trust Security . . . . . . . . . . . . . . . 16 4.2. Permission and Authorization Security . . . . . . . . . . 16 4.3. Data and Knowledge Security . . . . . . . . . . . . . . . 16 4.4. Tool and Supply Chain Security . . . . . . . . . . . . . 17 4.5. Behavior and Execution Security . . . . . . . . . . . . . 17 4.6. Lifecycle Security . . . . . . . . . . . . . . . . . . . 17 4.7. Observability and Audit Security . . . . . . . . . . . . 17 5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17 Sun, et al. Expires 20 January 2027 [Page 2] Internet-Draft AgentOps July 2026 6. Normative References . . . . . . . . . . . . . . . . . . . . 17 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 18 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 18 1. Introduction As large language model technologies continue to evolve from content generation toward task planning, tool invocation, and autonomous execution, agents operating within network environments—hereinafter referred to as agents—are gradually becoming important execution entities in network operations and maintenance, service operations, and network management. Agents are capable of invoking network management systems, operations and maintenance tools, digital twin platforms, knowledge bases, and model services to autonomously perform network operation tasks such as fault analysis, alarm correlation analysis, configuration generation, inspection analysis, and capacity optimization. They are also increasingly assuming some of the decision-making and execution responsibilities traditionally undertaken by human operators. Unlike conventional automation programs that operate according to predefined logic, agents can autonomously interpret tasks, plan execution procedures, select appropriate tools, invoke relevant resources, and complete task execution in accordance with operational objectives. Some agents are further equipped with persistent memory, experience accumulation, and multi-agent collaboration capabilities. As the autonomy and environmental adaptability of network operation agents continue to increase, they can no longer be regarded merely as static software programs. Rather, they have evolved into composite digital entities consisting of multiple interdependent elements, including models, prompts, network knowledge, runtime memory, tool plug-ins, Model Context Protocol (MCP) services, workflows, authorization policies, and execution environments. During the execution of network operations and maintenance tasks, an agent may dynamically adjust its execution strategy according to the current network state, service context, device capabilities, and the status of external tools. Consequently, even when processing the same operational task, different reasoning processes and execution outcomes may arise as a result of variations in model versions, knowledge content, runtime environments, or collaborating entities. At the same time, network operation agents may act on behalf of operations personnel or network management systems to access network resources, invoke operations and maintenance tools, execute configuration changes, and collaborate with other agents in completing complex network operation tasks. As the number and scale of network operation agents continue to grow, the models, knowledge, tools, network metric, permissions, and collaborative relationships Sun, et al. Expires 20 January 2027 [Page 3] Internet-Draft AgentOps July 2026 involved in their operation will also change continuously. In the absence of a unified management mechanism, a range of issues may arise, including limited visibility of agent assets, unclear accountability, poorly defined capability boundaries, excessive retention of permissions, insufficient observability of operational behavior, uncontrolled version evolution, and failure to reclaim resources following agent deactivation. These issues may, in turn, compromise network security and the stability of the overall network operations and maintenance. It is therefore necessary to pay more attention to the lifecycle management problems for agents operating in network environments, covering the entire process from registration and publication, deployment and operation, and continuous management to capability evolution, deactivation, and retirement. It should provide unified management of the key objects that continuously change throughout agent operation, ensuring that network operation agents remain discoverable, configurable, observable, assessable, controllable, and traceable, while continuously, securely, and reliably supporting the execution of network operations and maintenance tasks. 1.1. Scope The Agent focused in this draft is for network operation and management — how AI agents are configured, governed, and monitored, not AI applied to networking more broadly. Agent Lifecycle Management defined in this draft refers to a set of management policies, operational processes, and technical capabilities established for agents operating within network environments. It covers the entire lifecycle of an agent, from registration and publication, deployment and operation, and continuous operations to capability evolution, deactivation, and retirement. Focusing on core management objects—including agent identity, assets, capability configurations, knowledge, memory, tools, workflows, metrics, permissions, runtime behavior, versions, and collaborative relationships—it establishes mechanisms for unified governance, continuous observation, operational evaluation, risk control, change management, and decommissioning and resource reclamation. Its objective is to ensure that agents remain discoverable, configurable, observable, assessable, controllable, and traceable throughout their operational lifecycles, while enabling them to perform network- related tasks securely, reliably, and efficiently. Agent lifecycle management may considered by the following three components: Sun, et al. Expires 20 January 2027 [Page 4] Internet-Draft AgentOps July 2026 • End-to-End Operational Management: Agent lifecycle management encompasses the complete operational process of an agent, including registration and publication, deployment and operation, continuous operations, capability evolution, deactivation, and retirement. It is not limited to runtime monitoring or fault handling after an agent has been deployed. Lifecycle management requires the continuous maintenance and tracking of agent state transitions across different operational stages, ensuring that agents can operate reliably as network environments, service requirements, and operations and maintenance tasks evolve. Once an agent is withdrawn from operation, its identity must be deregistered, its permissions revoked, and its associated resources released, thereby establishing a complete closed-loop operational management process. • Multi-factor Collaborative Management: The operational capabilities of a network operations agent are jointly determined by multiple constituent factors. Accordingly, lifecycle management must address not only the agent software itself, but also its models, prompts, knowledge bases, memory, tools, MCP services, workflows, network metrics, identities, permissions, and runtime environments. Changes to any of these factors may affect the agent’s ability to understand network conditions, the manner in which it performs tasks, and the risks associated with its operation. Agent lifecycle management must therefore establish a unified management mechanism for all constituent factors and maintain their consistency, integrity, and traceability. • Continuous Operations and Dynamic Governance: The publication of an agent does not mark the completion of lifecycle management; rather, it marks the beginning of the continuous operational phase. As network topologies change, device versions are upgraded, knowledge continues to accumulate, models are iteratively updated, and operations and maintenance strategies are adjusted, the capabilities of an agent will continue to evolve. During operation, agents may also experience behavioral drift, tool failures, outdated knowledge, resource anomalies, and security risks. Lifecycle management must therefore continuously conduct runtime observation, performance evaluation, capability calibration, risk governance, and version evolution. Configurations, permissions, knowledge, and control policies should be dynamically adjusted according to the agent’s operational state, ensuring that the agent remains secure, reliable, and controllable throughout its entire operational lifecycle. Sun, et al. Expires 20 January 2027 [Page 5] Internet-Draft AgentOps July 2026 1.2. 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. 1.3. Definition and Terminology • AI：Artificial intelligence • LLM：Large Language Model • MCP：Model Context Protocol • NMS：Network Management System • OSS：Operations Support System 2. Why the Agent Lifecycle Management is important? As agents operating within network environments increasingly undertake tasks such as network monitoring, fault diagnosis, configuration changes, inspection analysis, capacity optimization, and network scheduling, they are evolving from auxiliary analytical tools into important execution entities in network operations. Network operation agents are capable of autonomously planning tasks, invoking models, accessing network resources, performing operations and maintenance actions, and collaborating with other agents to complete complex tasks. Their operational behavior may therefore directly affect network performance and service continuity. Conventional software operations and model management approaches are primarily designed for static applications and are insufficient to address the continuous evolution, autonomous decision-making, and dynamic collaboration of network operation agents. It is therefore necessary to establish a management mechanism that spans the entire operational lifecycle of network operation agents. 2.1. Network Operation Agents Are Evolving from Decision Support to Autonomous Execution, Increasing Operational Risks Traditional network intelligence systems primarily provide functions such as alarm analysis, fault prediction, and optimization recommendations, while final decisions and execution remain the responsibility of operations personnel. As agent capabilities continue to advance, network operation agents are increasingly able to autonomously perform tasks such as fault localization, Sun, et al. Expires 20 January 2027 [Page 6] Internet-Draft AgentOps July 2026 configuration generation, tool invocation, policy execution, and cross-system coordination. They are therefore evolving from auxiliary analysis toward autonomous execution. The enhancement of agent execution capabilities means that their influence is no longer limited to information generation but extends directly to network operation processes. Errors in task planning, abnormal tool invocation, or inappropriate execution strategies may result in incorrect configuration deployment, network service interruptions, or resource scheduling anomalies. Agent lifecycle management is therefore required to continuously safeguard the operational security and execution reliability of network agents. 2.2. Network Operation Agents Continuously Evolve, Making One-Time Management Approaches Insufficient The operational capabilities of network agents are jointly influenced by multiple factors, including models, knowledge, prompts, tools, network environments, and service policies. Their behavior may continuously change as models are upgraded, knowledge is updated, network topologies evolve, and tool capabilities are adjusted. Accordingly, the operational state of a network operation agent does not remain fixed after deployment. Reliance solely on pre-deployment testing or version acceptance cannot ensure long-term operational stability. Lifecycle management must extend throughout the agent’s operational process and continuously support runtime observation, capability evaluation, and dynamic optimization, ensuring that the agent remains aligned with the evolving network operating environment. 2.3. Network Operation Agents Depend on Multiple Capability factors and Require Unified Management The execution of network operations and maintenance tasks by network agents typically depends on the coordinated use of foundation models, network knowledge bases, digital twin platforms, monitoring systems, task ticket systems, network controllers, MCP services, and other agents. Changes to any of these constituent elements may affect the agent’s analytical results and execution behavior. As the scale of network operation agent deployment increases, complex dependencies emerge among models, knowledge, tools, network metrics, and collaborative relationships. In the absence of unified lifecycle management, issues such as unclear capability boundaries, configuration drift, tool failures, and collaboration anomalies may arise. Network agents and their associated capability elements must therefore be treated as integrated management objects and maintained continuously through a unified management mechanism. Sun, et al. Expires 20 January 2027 [Page 7] Internet-Draft AgentOps July 2026 2.4. Large-Scale Deployment of Network Operation Agents Requires a Unified Operational Governance Mechanism As the number of network operations agents continues to grow, different technical domains, platforms, and vendors may independently develop and deploy various types of agents that jointly participate in network operations. Without unified management, this may result in duplicated agent development, unclear accountability, uncontrolled identities and permissions, limited visibility into operational states, and prolonged occupation of network and computing resources. A unified governance mechanisms therefore required to cover the entire lifecycle of agent assets, identities, permissions, operational states, version evolution, decommissioning, and resource reclamation. It should enable unified onboarding, observation, and operation of network agents, thereby supporting the coordinated operation of agents at scale. 2.5. Network Operation Agents Should Continuously Create the Value of Operation The objective of deploying network operation agents is not merely to complete their development, but more importantly to continuously improve network operations efficiency, reduce fault recovery time, enhance network performance, and decrease reliance on manual intervention. Lifecycle management should therefore extend beyond the deployment stage and continuously evaluate agent performance, resource consumption, operational costs, and business contributions. Through continuous evaluation and dynamic optimization throughout the operational lifecycle, issues such as capability degradation, outdated knowledge, excessive operational costs, and declining business value can be identified in a timely manner. The operational capabilities of network operation agents can then be continuously optimized to ensure that they remain secure, stable, and efficient while consistently contributing to the improvement of network operations and maintenance capabilities. 3. Requirements for Agent Lifecycle Management As agents increasingly undertake operations and maintenance tasks such as network monitoring, fault diagnosis, configuration changes, inspection analysis, capacity optimization, and network scheduling, the scope of lifecycle management is no longer limited to the agents themselves. It has expanded to encompass multiple dimensions, including identity, capabilities, knowledge, tools, behavior, resources, and security. Throughout different lifecycle stages, agents continuously undergo capability evolution, knowledge updates, tool expansion, and changes in operational status. Agent lifecycle management must therefore provide unified management of the key Sun, et al. Expires 20 January 2027 [Page 8] Internet-Draft AgentOps July 2026 objects that continuously change during agent operation, ensuring that agents remain identifiable, configurable, observable, controllable, and traceable throughout their entire lifecycles. 3.1. Agent Asset and Accountability Management The primary objective of agent asset and accountability management is to determine which agents are operating within the network, what tasks they are responsible for, who is accountable for them, and how they are associated with network resources and systems. The relevant management information typically includes the agent’s unique identifier, name, source, functional role, technical domain, deployment location, operational status, accountable entity, and relationships with network devices, service systems, and other agents. In network operations and maintenance scenarios, an agent possesses not only the general attributes of a software asset but may also be characterized through five key elements: role responsibilities, runtime environment, behavioral constraints, domain knowledge, and tool-related skills. Collectively, these elements define the agent’s role, capability boundaries, and execution methods within the network operations and maintenance system, thereby facilitating a consistent understanding of the functions and responsibilities of different agents. As an increasing number of agents are developed by different departments, platforms, and vendors, major challenges include inconsistent agent identification, limited exchangeability of asset information, unclear accountability relationships, and difficulties in cross-platform discovery and onboarding. * Gap analysis for IETF standards: 1.It may need to establish requirements for unified agent asset onboarding and management, covering unique identifiers, names, sources, functional roles, technical domains, deployment locations, operational states, accountable entities, and relationships with network devices, service systems, and other agents. 2.Define the five key elements of an agent to describe its role, capability boundaries, and execution methods within the network operations and maintenance system. 3.2. Agent Capability Composition and Configuration Management The capabilities of a network operation agent are not derived from a single model. Rather, they are jointly determined by multiple elements, including LLM models, prompts, knowledge bases, memory, tools, workflows, runtime policies, and model parameters. When an agent performs tasks such as network fault diagnosis, configuration changes, and inspection analysis, its capability boundaries and execution results depend on the coordinated operation of these Sun, et al. Expires 20 January 2027 [Page 9] Internet-Draft AgentOps July 2026 constituent elements. Agent lifecycle management must therefore manage not only the agent itself but also the composition and configuration state of its capabilities on a continuous basis. Network operations environments are characterized by continuous device upgrades, rapid knowledge updates, ongoing expansion of tool capabilities, and dynamic adjustments to operational policies. Consequently, the constituent elements of an agent also change continuously. Managing only software code or model versions, without unified management of configuration elements such as prompts, knowledge, tools, workflows, and runtime policies, makes it difficult to accurately reproduce an agent’s capability state or analyze the effects of capability changes on network operations tasks. A unified management mechanism covering agent capability composition and configuration elements is therefore required to ensure that capability composition can be formally described, configuration states can be tracked, and capability changes can be audited. * Gap analysis for IETF standards: It may need to establish unified definitions and management mechanisms for the elements that constitute and configure agent capabilities. 3.3. Network Performance Parameters, Knowledge, Ontology, and Memory Management Network performance metrics, knowledge, ontologies, and memory collectively constitute the information foundation upon which network agents perform tasks. Network performance parameters represent the operational state of the network; ontologies define network entities and their semantic relationships; knowledge provides operational rules and expert experience; and memory retains contextual information and historical experience generated during agent operation. Together, these elements support agents in performing complex tasks such as network fault diagnosis, root-cause analysis, and configuration optimization. As network environments and services continue to evolve, data, knowledge, ontologies, and memory also change continuously and must therefore be maintained as important lifecycle management objects. Network operations and maintenance scenarios are characterized by heterogeneous data sources, frequent knowledge updates, and complex relationships among network entities. In the absence of unified management, problems may arise, including inconsistent data provenance, incompatible ontology definitions, outdated knowledge, and memory contamination. Such problems may impair an agent’s ability to understand network conditions and effectively perform tasks. It is therefore necessary to establish a unified management mechanism for data, knowledge, ontologies, and memory, enabling data provenance to be traced, knowledge to be updated, ontologies to be Sun, et al. Expires 20 January 2027 [Page 10] Internet-Draft AgentOps July 2026 shared, and memory to be governed. This mechanism should provide a consistent and trustworthy knowledge foundation for the continuous operation of network agents. * Gap analysis for IETF standards: 1.It may need to establish a unified management mechanism for Network performance metrics, knowledge, ontologies and related data models and memory, including unified definitions of Network performance metrics, knowledge, and ontologies, as well as relevant ontology models. 3.4. Tool, Skill, and Collaboration Relationship Management The ability of network operation agents to evolve from information analysis toward autonomous task execution depends primarily on their ability to invoke network tools, apply specialized skills, and collaborate with other agents in completing complex operations and maintenance tasks. Tools, skills, and collaborative relationships therefore define the capability boundaries of an agent and constitute important management objects within agent lifecycle management. Lifecycle management must continuously maintain the tools, skills, and collaborative relationships available to an agent. These may include network management systems, operations support systems, monitoring platforms, configuration centers, digital twin platforms, task tickets systems, network controllers, and various tool interfaces based on the Model Context Protocol. It must also maintain specialized skills and invocation relationships associated with fault diagnosis, alarm analysis, configuration changes, inspection, and optimization. As network operations increasingly evolve toward multi-agent collaboration, a complex operations and maintenance task may require the coordinated participation of multiple agents. Such agents may form complex relationships involving task decomposition, capability invocation, information sharing, and collaborative execution. Without unified management, issues may arise, including inconsistencies in tool capabilities, interface invocation failures, invalid collaborative relationships, and unclear capability boundaries. A unified management mechanism for tools, skills, and collaborative relationships is therefore required to continuously maintain tool versions, interface status, capability dependencies, and collaboration relationships, thereby ensuring that network agents can perform cross-system collaborative tasks reliably and efficiently. Sun, et al. Expires 20 January 2027 [Page 11] Internet-Draft AgentOps July 2026 * Gap analysis for IETF standards: 1.It may need to establish a unified management mechanism for tools, skills, and collaboration relationships, including standardized definitions of interface status, capability dependencies, and inter-agent collaboration relationships. 3.5. Identity, Authorization, and Trust Relationship Management As network agents increasingly participate in network configuration, resource management, and automated operations, they can no longer be regarded merely as conventional software programs. Rather, they function as digital subjects capable of performing network tasks on behalf of operations personnel, service systems, or other agents. Agent lifecycle management must therefore establish a unified identity, authorization, and trust management mechanism to ensure that agents possess explicit identities, legitimate permissions, and trustworthy execution capabilities throughout their operation. Such management must continuously maintain agent identity information, identity credentials, accountable entities, authorization sources, access permissions, operational scope, and trust relationships, ensuring that every resource access request, tool invocation, and operations action can be attributed to a specific agent and its authorization source. The responsibilities and risk levels of network operations agents vary considerably, and their access privileges and execution scopes should therefore differ accordingly. For example, an inspection agent may be permitted only to query network status, whereas a configuration change agent may be authorized to modify network configurations. In the absence of unified identity, authorization, and trust management, problems such as identity sharing, excessive authorization, unclear accountability, and unauthorized agent execution may arise. It is therefore necessary to establish a management mechanism covering the entire process of agent identity creation, authorization, modification, suspension, and revocation. Agent permissions and trust relationships should be dynamically adjusted according to lifecycle status to ensure the continuous, secure, and trustworthy operation of network agents. * Gap analysis for IETF standards: 1.It may need some operational security best current practices. Sun, et al. Expires 20 January 2027 [Page 12] Internet-Draft AgentOps July 2026 3.6. Quality, Effectiveness, and Performance Management As network agents increasingly undertake production tasks such as fault diagnosis, configuration changes, network optimization, and automated remediation, their operational quality directly affects network stability and operations efficiency. Agent lifecycle management must therefore assess not only whether an agent can complete a task, but also the quality of task execution, operational effectiveness, and business value delivered by the agent. Lifecycle management should continuously maintain quality indicators for different stages of agent operation, including task completion rate, fault localization accuracy, tool invocation success rate, automated remediation success rate, response latency, stability, and robustness. These indicators should be combined with measures of network performance, operational efficiency, and resource consumption to continuously evaluate agent performance. Because network environments, model capabilities, knowledge content, and tools change continuously, agent performance may also vary over time. Pre-deployment testing alone is therefore insufficient to guarantee long-term reliability. A continuous evaluation mechanism spanning the entire operational lifecycle of an agent is required to support dynamic monitoring of operational quality, continuous validation of capabilities, and ongoing assessment of business value. This mechanism should ensure that agents remain stable, efficient, and trustworthy while continuously contributing to the improvement of network operations and maintenance capabilities. * Gap analysis for IETF standards: 1.It may need to define quality metrics to different stages of agent operation, and establish an agent performance evaluation mechanism incorporating indicators such as network performance, operations and maintenance efficiency, and resource consumption. （This work related to evaluate the performance of AI agents ). The IETF BMWG has active work on AI benchmarking that is directly relevant; work in this area should coordinate with and reference that effort. 3.7. Runtime Behavior, Reliability, and Cost Management NNetwork agents possess autonomous planning, reasoning, decision- making, and execution capabilities. Their operation involves not only task inputs and result outputs, but also task interpretation, reasoning processes, tool invocation, multi-agent collaboration, and execution feedback. Agent lifecycle management must therefore continuously maintain information relating to runtime behavior, service reliability, and resource consumption to ensure that agents can perform network operations and maintenance tasks reliably, securely, and efficiently. Lifecycle management should continuously Sun, et al. Expires 20 January 2027 [Page 13] Internet-Draft AgentOps July 2026 collect information concerning task requests, contextual data, reasoning processes, tool invocations, execution results, anomalous events, and operational states. This information should be combined with indicators such as response latency, task success rate, token consumption, number of model invocations, and resource utilization to create a comprehensive behavioral profile covering the entire agent execution process. Network operations and maintenance tasks are characterized by stringent real-time requirements, continuous operation, and cross- system collaboration. An anomaly occurring at any stage may affect the entire operational workflow. It is therefore necessary to establish an observability mechanism covering the complete agent execution process, ensuring that agent behavior can be recorded, execution processes can be traced, causes of anomalies can be identified, resource consumption can be measured, and operational costs can be quantified. Agent reliability should also be continuously evaluated to support operational optimization, resource scheduling, and cost control. * Gap analysis for IETF standards: It may need to define an end-to- end observability mechanism covering the complete execution process of agents, including the observability of Model Context Protocol (MCP) services, LLM models, and agents. 3.8. Security, Risk, Compliance, and Audit Management As network operation agents increasingly participate in critical operations and maintenance tasks such as network configuration, resource scheduling, and fault recovery, their security risks have expanded beyond conventional software security to encompass models, data, tools, identities, and multi-agent collaboration. Agent lifecycle management must continuously maintain information concerning agent security boundaries, risk levels, permission scopes, tool authorizations, sensitive data access, security policies, and audit records. Key events occurring throughout agent operation must be continuously recorded to ensure that agents perform network tasks securely and reliably within their authorized scope. During operation, network agents may be exposed to emerging risks such as prompt injection, knowledge poisoning, tool misuse, unauthorized access, incorrect configuration deployment, and risk propagation across multiple agents. A security governance mechanism spanning the entire agent lifecycle is therefore required to support continuous risk identification, dynamic adjustment of security policies, anomalous behavior detection, and end-to-end audit tracing. When an agent performs a high-risk network operation, the basis for the action, authorization source, scope of impact, and accountable Sun, et al. Expires 20 January 2027 [Page 14] Internet-Draft AgentOps July 2026 entity should be explicitly identified. The complete execution process should support traceability and accountability auditing to ensure the secure and compliant operation of network agents. * Gap analysis for IETF standards: It may need some operational security best current practices. 3.9. Version Change, Evolution, and Retirement Management Network operation agents are continuously evolving entities whose capabilities change as foundation models, knowledge bases, tools, prompts, and operations policies are updated. Agent lifecycle management must therefore address not only the operational status of an agent but also its version evolution, capability changes, and retirement process, ensuring that every change is identifiable, verifiable, and traceable. Lifecycle management should maintain agent version information, change descriptions, scopes of impact, evaluation results, release status, and relationships among versions. It should also continuously record changes to key capability elements, including models, knowledge, tools, and configurations. As network devices are upgraded, service requirements change, and operations capabilities continue to evolve, some agents may be withdrawn from production environments because their capabilities have been replaced, their corresponding services have been adjusted, or they have remained inactive for extended periods. Agent retirement involves more than terminating operation. It also requires identity deregistration, permission revocation, tool disassociation, knowledge and memory cleansing, termination of collaboration relationships, and archival of operational data. A lifecycle management mechanism covering version release, upgrade, modification, replacement, deactivation, and retirement is therefore required to support continuous agent evolution and secure withdrawal, while maintaining consistency between operational status and capability configuration. * Gap Analysis: 1.It may need to establish a lifecycle management mechanism covering the complete processes of version release, upgrade, modification, replacement, deactivation, and retirement. Section 7 of [RFC 9222] addresses upgrade and lifecycle management for autonomous agents and is directly relevant existing work in this area, Sun, et al. Expires 20 January 2027 [Page 15] Internet-Draft AgentOps July 2026 4. Security Considerations Agent lifecycle management should address not only the continuous evolution of agent capabilities, but also the security of agents throughout their entire operational lifecycles. Network agents possess capabilities for autonomous planning, reasoning, tool invocation, and task execution. They may also act on behalf of users or systems to access network resources, invoke service interfaces, and perform operations and maintenance actions. Consequently, their security boundaries have expanded beyond conventional software security to encompass identity, authorization, data, knowledge, tools, collaboration, and runtime behavior. Agent lifecycle management should therefore establish continuous governance mechanisms around critical security objects, ensuring that agents remain trustworthy, controllable, and auditable throughout their operation. 4.1. Identity and Trust Security As independent digital subjects, network agents must possess unique identities and trustworthy identity authentication capabilities. Lifecycle management should address security issues such as identity forgery, identity impersonation, cross-domain authentication, and the establishment of trust relationships among agents. It should ensure that agent identities remain authentic and trustworthy throughout the entire lifecycle and that stable trust relationships can be established and maintained. 4.2. Permission and Authorization Security Network operatoin agents may access network resources and perform operations and maintenance actions; therefore, their permissions must be commensurate with the tasks they undertake. Lifecycle management should continuously address issues such as excessive authorization, permission misuse, invalid delegation relationships, and the prolonged retention of highly privileged agents. Agent authorization should consistently comply with the principle of least privilege. 4.3. Data and Knowledge Security During operation, agents continuously access network data, knowledge bases, and long-term memory. The security of these information assets involves not only data confidentiality, but also knowledge integrity, data authenticity, and memory trustworthiness. Lifecycle management should address risks such as sensitive data leakage, knowledge poisoning, training data tampering, residual long-term memory, and untrustworthy data provenance. Sun, et al. Expires 20 January 2027 [Page 16] Internet-Draft AgentOps July 2026 4.4. Tool and Supply Chain Security Network operation agents depend on a wide range of external tools, Model Context Protocol services, models, plug-ins, and third-party capabilities. Their security risks therefore extend across the entire agent supply chain. Lifecycle management should continuously assess tool trustworthiness, interface security, model provenance, plug-in integrity, and operational risks arising from changes to external capabilities. 4.5. Behavior and Execution Security Because agents possess autonomous reasoning and execution capabilities, their behavior may drift as a result of changes in context, prompts, models, or knowledge. Lifecycle management should continuously address operational security risks such as prompt injection, malicious instructions, inappropriate tool invocation, incorrect configuration deployment, and the propagation of erroneous behavior across multiple agents. Appropriate controls should be established to ensure that agent behavior remains aligned with intended objectives and operational constraints. 4.6. Lifecycle Security New security risks may arise at every stage of a network agent’s lifecycle, including registration, operation, upgrade, modification, deactivation, and retirement. Lifecycle management must ensure that model upgrades, knowledge updates, tool changes, permission adjustments, and agent retirement processes are verifiable, auditable, and traceable. It is necessary to prevent residual identities, unreclaimed permissions, retained data, and uncontrolled capabilities resulting from inadequate lifecycle management. 4.7. Observability and Audit Security Critical events throughout the agent lifecycle should be continuously recorded and traced, including identity changes, permission adjustments, tool invocations, model updates, knowledge updates, runtime anomalies, and collaborative interactions. Agent lifecycle management should establish a unified security audit mechanism to support operational analysis,accountability tracing, incident investigation, and security forensics. 5. IANA Considerations This document has no IANA actions. 6. Normative References Sun, et al. Expires 20 January 2027 [Page 17] Internet-Draft AgentOps July 2026 [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, . [RFC9222] Carpenter, B., Ciavaglia, L., Jiang, S., and P. Peloso, "Guidelines for Autonomic Service Agents", RFC 9222, DOI 10.17487/RFC9222, March 2022, . Acknowledgements TBD Authors' Addresses Qiong Sun China Telecom Beijing China Email: sunqiong@chinatelecom.cn Runze Luo China Telecom Beijing China Email: luorz4@chinatelecom.cn Yu Fu China Telecom Beijing China Email: fuy44@chinatelecom.cn Chongfeng Xie China Telecom Beijing China Email: xiechf@chinatelecom.cn Sun, et al. Expires 20 January 2027 [Page 18]