据行业最新消息，The Constraint Infrastructure Growing on Alibaba Cloud Agent Infra
Agent = Model + Harness. This formula has gained market consensus because it very concisely summarizes the relationship among Agent, Model, and Harness, and provides a clear direction of investment for improving Agent quality. However, Harness only answers directional questions. In practice, the engineering team faces a set of concrete platform requirements: How are constraint rules declared and version-managed? How do rule changes take hot effect in seconds without restarting services? How do constraint execution points cover every stage of the Agent lifecycle, from model calling to task orchestration to O&M observation? How are abnormal behaviors detected in real-time to trigger interception or degradation? How do the constraint rules themselves continuously iterate along with the evolution of Agent capabilities, rather than becoming obsolete once written? These requirements exceed the scope that the methodology itself can cover; they need more concrete platform capabilities and infrastructure to support them. This is far more complex than the simple formula Agent = Model + Harness. The article “What Does Alibaba Cloud Agent Infra Look Like” answered the six infrastructure capabilities of Alibaba Cloud Agent Infra, including Agent Runtime, Agent Orchestration, Agent Governance, Agent Memory, Agent Data Plane, and Agent Security, to address unpredictable burst loads of Agents, large-scale dynamic orchestration of Agents, short life cycles of Agents, complex Agent data modalities and storage forms, Agent dynamic environment dependencies, and task-level safety and control of Agents. Therefore, we have further decomposed Harness. Harness = Define Constraints + Validate Outputs + Establish Feedback Loop Among them, the constraint infrastructure refers to the platform capabilities and infrastructure within the Agent Infra that support Harness in constraining Agent behavior. The definition of constraint infrastructure is: the infrastructure layer that systematically guarantees the runtime behavior boundaries of Agents, responsible for transforming the constraint principles in the Harness methodology into programmable, deployable, and operational engineering entities. Specifically, constraint infrastructure needs to provide the following capabilities: declarative definition and version control of constraint rules, dynamic distribution and hot activation of rules during runtime, unified embedding of constraint execution points at all stages of the Agent lifecycle, real-time detection, interception, and automatic restoration of non-compliant behaviors, as well as observability and auditability of constraint effectiveness. Constraint infrastructure does not replace existing Agent development frameworks or inference engines; it is a governance layer running on top of these components. Development frameworks focus on how Agents complete tasks, while constraint infrastructure focuses on which boundaries Agents must not cross while completing those tasks. According to the lifecycle of an Agent request, the constraint infrastructure can be disassembled into four layers. Each layer corresponds to a clear set of constraint responsibilities. Agent systems typically need to connect to multiple model providers, mixing models of different capability levels and costs. The constraint responsibility of the model access layer is to centrally manage and control the access policies of all model calls at the gateway level, rather than having each Agent application implement its own rate limiting and authentication. As Alibaba Cloud’s open-source AI gateway, Higress plays a core role in this layer. It provides model-level traffic control capabilities: unified routing of multiple model providers (dispatching requests to appropriate models based on task type), token-based usage limiting (unlike traditional QPS rate limiting, AI scenarios require quota management based on actual token consumption), and access policies for model calls. Alibaba Cloud API Gateway’s AI gateway capability further provides enterprise-level features on top of the open-source version of Higress: fully managed identity authentication and authorization, multi-dimensional control of calling quotas (by user / by application / by model), and mandatory validation of output format contracts. For the constraint infrastructure at the traffic ingress, model access policies are infrastructure-level concerns that should sink to the gateway for unified processing, rather than being scattered in the code of each Agent application. The traffic ingress layer controls model invocation behaviors, while the Agent runtime behavior controls the execution process of the Agent itself. This layer needs to answer three questions: How is the Agent’s Prompt behavior centrally managed and dynamically adjusted? How is the execution process of a single Agent observed and constrained? How is multi-agent collaboration made access-controlled and behavior-auditable? Prompt as the core carrier of behavioral constraints. Prompt is the most direct means to define an Agent’s behavior boundaries, but in a production environment, Prompts cannot be hardcoded strings in code. MSE Nacos AI manages Prompts as first-class configuration assets, providing a centralized asset library (unified storage of Prompts for all Agents), semantic version control (saving 30-day historical versions by default, supporting one-click rollback), sub-second hot updates (taking effect immediately after modification without restarting the application), and canary release strategies (canary by IP or by tag to reduce change risks). When the behavior boundary of a certain type of Agent needs to be tightened, the governance team modifies the Prompt and gradually verifies it through a canary release, without needing to redeploy business code throughout the process. Observation-driven dynamic constraints. Static rules can only cover known patterns, whereas Agent abnormal behaviors in production environments (dead loops, output drift, tool abuse) often need to be intercepted based on runtime data. AgentLoop provides full-link tracing capabilities for LLM applications, automatically collecting golden indicators such as Token consumption, TTFT (Time to First Token), and TPOT (Time per Output Token), and connecting the complete path from user request to model inference based on the OpenTelemetry GenAI extension specification. On this basis, AgentLoop’s evaluation system supports automated verification in three scenarios: basic LLM dialogue evaluation, RAG process evaluation, and Agent tool call evaluation, covering toxic content detection, safety review, content relevance, and tool selection accuracy. These evaluation capabilities constitute the engineering implementation of the “Validate Outputs” phase in Harness. Collaborative governance of multi-agents. When multiple Agents collaborate, the complexity of constraints rises significantly. AgentTeams, as an enterprise-grade multi-agent governance and collaboration platform, implements collaborative orchestration through a Leader-Worker architecture: the Leader is responsible for task decomposition and dispatch, and the Worker is responsible for execution. This architecture itself is a constraint design; Workers can only execute tasks dispatched by the Leader and cannot expand their action scope on their own. At the security level, AgentTeams achieves fine-grained permission control based on a zero-trust security model, where all communications between Agents are decoupled at the protocol level based on the Matrix protocol, supporting unified take-over of multi-source heterogeneous Agents. Instance-level resource isolation ensures that Agents from different business scenarios do not interfere with each other. The monitoring dashboard provides real-time visualization across three dimensions: Worker statistics, tasks and teams, and model calls, allowing the effectiveness of constraint execution in multi-agent environments to be continuously observed. 2.1 and 2.2 defined “what to constrain,” while 2.3 solves “how to dynamically manage constraint rules” and “how to run Agent tasks within resource boundaries.” Four Registries build a unified governance plane for AI assets. Starting from version 3.0, MSE Nacos AI extends the concept of registry from microservices to AI scenarios, constructing four major registries: Prompt Registry, MCP Registry, Agent Registry (A2A), and Skill Registry. The MCP Registry supports upgrading legacy HTTP interfaces to the MCP protocol with zero code modification and provides hot-update capabilities for Tool metadata. When the description or parameter definition of a tool needs to be modified, the configuration takes effect automatically, and all Agents using that tool immediately obtain the updated metadata. The Agent Registry achieves registration and discovery among Agents based on the A2A protocol, supporting namespace isolation and multi-version management. The Skill Registry provides a review mechanism for skills before they go online and sub-second rollback capabilities, ensuring that new skills will not be called by Agents without validation. Task orchestration and resource boundary control. MSE AI Task Scheduling extracts scheduled scheduling from the inside of each Agent and manages it centrally through the platform. It provides a four-level priority queue (low / medium / high / very high), allowing high-priority tasks to preempt resources from low-priority tasks; failed auto-retries support configuring the number of retries and intervals, which can be dynamically adjusted in the console; timeout alerts and failure alerts provide timely anomaly notifications. Visual DAG orchestration supports defining task dependencies across applications, avoiding deadlocks and cyclic waiting. Real-time routing and automatic response of constraint events. When the execution points at various layers of constraints (Higress gateway, AgentLoop evaluation, AgentTeams permission
随着IDC行业的快速发展，可持续发展将成为未来竞争的关键
如果您正在寻找优质的香港云服务器，欢迎访问 www.isclouder.com 了解更多