重新尺度是如何与 Strands Agents 和 AI-DLC 一起构建一个 AI 分类的 agent

人工智能

重新尺度是如何与 Strands Agents 和 AI-DLC 一起构建一个 AI 分类的 agent

每个工程队都知道仪式，而重新规模也不例外。 一个夜间测试套房失败。 随叫随到的工程师拉动日志，对根源进行分类，并把它引向正确的队伍，只在第二天早上重复这一过程。 在规模上，在分布式服务和数十个寄存处之间，这种仪式成为了隐藏在待命轮换中的全职工作。

缩放是为AI时代建造的数字工程平台。 研发团队和工程团队跨越航空航天，汽车，能源，生命科学，半导体，以及制造业等多个领域，利用调整尺度平台加速创新，将智能HPC，高级建模和模拟，代理数字工程，AI物理等多个平台并举。

除了编码外，再规模在整个工程生命周期使用AI，从规划和设计到执行，测试，审查，操作，同时让工程师对结果负责。 AI可以提议和执行，但人们可以定义范围，验证工作，批准生产变更。 然后工程师可以专注于建筑，产品判断，以及操作复杂分布式系统所需的背景。

在本文中，我们展示了Rescale如何在AWS上使用AI-Driven开发生命周期(AI-DLC)框架来构建一个智能的分型agent. 他们从问题定义到5天后部署，再到几周内生产。 也报导Rescale如何将AI-DLC扩展至他们的工程组织。

挑战

随着Rescale平台的增长，维持平台运行的业务间接费用也随之增加。 工程师们花费了大量时间来完成一项重复而关键的任务：对自动端到端测试故障进行分类。 成百上千个测试案例在套房中运行，当出现故障时，每一个警报，错误报告，升级都需要分类，优先排序，并被引导到正确的团队。 在通路后，工程师需要从多个伐木和监测系统收集证据，以及若干源寄存器，然后才能开始调试。 需要通过端到端测试才能将服务部署到生产中，因此调试这些系统故障是取得生产新变化的关键途径。

在手动工作流程中，工程师从测试报告转向日志，历史模式，以及源代码，对失败进行分类，并找出可能拥有的团队。 由于这些集成测试门每周制作发布，未解决的故障可能会在允许部署之前将几个团队拖入数小时的协调。

团队对解决方案的外观有清楚的认识：一个能够自动分类和路由问题的智能agent. 他们所寻求的是从这种理解发展到生产工作系统的结构化方法，其结构足以将方法扩大到以后的其他使用情况。

为什么缩放选择 AI-DLC

agent是一个范围很广的问题，工作队需要一种发展办法，在不跳过将决定该系统是否在生产中发挥作用的建筑决定的情况下，能够以这种速度前进。

AI-Driven开发生命周期(AI-DLC)是一种开源，以AI为中心的软件开发方法，它围绕两个想法：

- AI执行，人类决定：AI生成工作计划，写代码，并提议架构，但它会浮出决定点，提出澄清问题而不是猜测。 人类在每个步骤上验证和引导。

- 团队会议取代互赠：AI-DLC不单是一人大楼，而是要求以后进行审查，而采用重点小组会议：Mob Complexing(共同界定要求)和Mob Building(共同建设，同时AI处理执行)。 决策是实时的。

AI-DLC很合适，它确保AI理解团队想要建设的东西。 在暴徒会议中，重组工程师共同回答了AI的问题，并在AI前进之前批准了每一个计划，因此没有留下任何假设。

解决方案概述

调整分级的agent将夜间端到端测试故障的分类和路径自动化。 它作为Rescale现有Argo Workflow的连续集成和连续交付(CI/CD)管道在Amazon弹性Kubernetes服务(Amazon EKS)上的一个步骤运行。 当一个夜游的Playwright套装上数百个测试案例产生故障时，管道触发了分型agent的吊舱。

agent 执行以下步骤：

- Claude Opus 4.8在Amazon Bedrock上将每次故障分类为根因类：基础设施问题、代码错误、片面测试或环境问题。

- 从知识库中获取相关的历史失败模式，以补充背景。

- 通过模式上下文协议(MCP)查询用于支持分类的运行时间证据。

- 在采取任何行动之前，生成一份结构化补救报告，其中包含具体的参考文献、功能名称和建议的修复方法供工程师审查。

agent以几秒钟完成这个工作流程，并直接输出到团队现有的报告管道中。 下图显示这些组件如何组合：

AI-DLC研讨会经验

参与安排为为期五天的亲自讲习班。 AWS建筑师指导建造，而调整规模的工程师则手动操作键盘，实时协同建造。

开始阶段(第1-2天)：从愿景到经验证的要求

小组首先从范围界定要求入手，在构想文件和技术环境文件中抓住意图，在任何大楼开始之前就把工作作为基础。 这一意图从一个单一的语句开始，即“我们希望AI能对失败进行分类并建议补救的测试分类”。 团队以暴徒形式合作，回答了AI产生的20+澄清问题。 这产生了一份范围界定的要求文件，其中明确划定了第一阶段的界限，并推迟了第二阶段的项目。 5个用户故事合并为4个工作单元：

- 历史失败模式的知识基础。

- AI 分类agent作为核心分类和补救引擎。

- 启动、基础设施和部署的Argo Workflow集成。

- 精确度测量和回归度测量的评价方法。

人类的决定驱使着关键界限：属于第1阶段相对于第2阶段，优先排序，以及诸如自动拉力请求生成(PR)等延期特性。

施工阶段(第2-4天)：运行代码的架构

该小组界定了接口合同和数据计划，完成了功能和非功能设计问题，并在架构上签字。 建造期间出现了两个建筑小区，队伍在改变航线之前对每个小区进行了验证。

首先，球队计划在Amazon Bedrock Agent Core上托管agent，然后将其转移到Amazon EKS，在那里，Rescale已经运行了其Argo Workflows CI/CD管道。 将agent保留在现有集群中，意味着没有新的运行时间可以运行，也是从Argo直接触发路径。 第二，在早期的建设中，agent通过MCP从第三方伐木服务中提取了日志证据。 团队将日志检索移至Amazon Cloud Watch，这简化了依赖链。 MCP模式保持不变。 只有背后的源头改变了由于AI-DLC在共享文件中保留了要求和设计决定，因此团队更新了这些要求和决定并保持了建设不失去上下文。

agent是使用Strands SDK建造的，支持同时进行故障分析，在JSON和HTML中以结构化输出，在Amazon EKS上装箱。

到第4天，整个管道正在运行中：Argo工作流程触发器、分解agent舱、MCP日志查询、知识库检索和结构分解输出。

业务阶段(第5天)：评价和移交

该小组建立了基于PyTest的评价框架，采用活式和悬浮式测试模式，建立了初步分类-协议基线，以衡量agent的对比和改进。 agent被降级为rescale的CTO和VP of Engineering，正确分类了一个基础设施故障和一个带有特定文件级固定建议的真正的代码错误。

AI-DLC 如何缩小复杂程度

并不是每个单位的工作都得到同样的待遇。 知识库需要最低限度的设计，并在数小时内使用Terraform。 Triage Agent获得了全刚性：架构审查，接口合同，12个设计问题，以及迭代代码审查。 阿尔戈一体化的重点是基础设施合同和IAM。 "评价哈尼斯"采用轻量级设计但重度执行。 AI-DLC 自动匹配深度和复杂度。

到了第5天末，agent在中转中直播，以测量精度基线和明确的生产路径处理真正的测试故障。

生产成果

分流器agent自车间开始投入生产。 它自动将夜间端到端的测试故障分类，用工程师验证的自动首发通道取代手动首发分录。 对于每次失败，agent生成一份结构化的补救报告，其中包含可操作的下一步步骤，而不是原始日志。 agent与人类分型的分类协议从最初部署时的44%上升到今天的68%. 随着团队在金色数据集和即时工程上的延展，它继续得到改善。

自分解agent进入生产后，团队测得在分析测试故障的同时，时间阻断生产部署减少20%. 因为这些测试门调整规模的每周生产发布，减少显示直接在如何快速的改变到达生产。 工程师在重复分类方面也花费较少的待命轮调费用。

讲习班结束后的生活：从一个agent到AI-第一练习

讲习班提供了一种生产agent，但其更大的影响在于如何采用再规模方法进行工程。 分型agent是在Rescale为AI时代提供数字工程能力的更广泛推动的一部分。 在内部，再规模正在将AI在自己的工程组织中实现的这些相同效益应用到其中。 AI-DLC给了Rescale的工程师一种有条理的，可重复的方式，将这个方向付诸实践。

在整个组织内扩大AI-DLC

调整规模正在评估一个内部AI-DLC英才中心，这是一个将最佳做法编码并帮助其他团队独立采用该方法的小组。

参与的工程师已经在应用他们学到的知识。 一位工程师指出，这一进程“将是我今后工作流程的一部分”，不仅是为了建造agents，而且也是为了AI能够作出贡献的任何复杂的开发工作。

更广泛的人工智能本土发展

缩放目前正在将AI-DLC纳入新工程模拟产品的标准软件开发生命周期。 框架带来模拟-域专业知识，产品要求，平台架构，验证，运行为一个结构化的设计过程。 团队提前解决关键问题，以可执行规格获取经过审查的决定，并以明确的接受标准给予agents的集中工作。 这缩短了从产品概念到验证实施的道路，同时工程师仍对产品正确性负责。

在产品授权平台工作和agent系统中已经采用了同样的方法：

- HPC模拟应用目录管理。

- 对集装箱的脆弱性进行跟踪和补救，以满足政府遵守要求的要求。

- 基于工作流的云码agents.

结论

对于分型agent,AI-DLC最有用的输出并不是代码本身。 这是从最初设想到审查系统设计的结构化道路：澄清问题、范围要求和评价

本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。

Artificial Intelligence

How Rescale built an AI triage agent with Strands Agents and AI-DLC

Every engineering team knows the ritual, and Rescale was no exception. A nightly test suite fails. An on-call engineer pulls the logs, classifies the root cause, and routes it to the right team, only to repeat the process the next morning. At scale, across distributed services and dozens of repositories, this ritual becomes a full-time job hiding inside an on-call rotation.

Rescale is the digital engineering platform built for the AI era. R&D and engineering teams across aerospace, automotive, energy, life sciences, semiconductor, and manufacturing use the Rescale platform to accelerate innovation, bringing intelligent HPC, advanced modeling and simulation, agentic digital engineering, and AI physics together on a single platform.

Beyond coding, Rescale uses AI across the engineering lifecycle, from planning and design through implementation, testing, review, and operations, while keeping engineers accountable for the result. AI can propose and execute, but people define scope, validate the work, and approve production changes. Engineers can then focus on architecture, product judgment, and the context required to operate complex distributed systems.

In this post, we show how Rescale used the AI-Driven Development Lifecycle (AI-DLC) framework on AWS to build an intelligent triage agent. They went from problem definition to staging deployment in five days, and to production within weeks. The post also covers how Rescale is extending AI-DLC across their engineering organization.

The challenge

As Rescale’s platform grew, so did the operational overhead of keeping it running. Engineers were spending significant time on a repetitive but critical task: classifying automated end-to-end test failures. Hundreds of test cases run in the suite, and when failures appeared, every alert, bug report, and escalation needed to be classified, prioritized, and routed to the right team. After routing, engineers would need to gather evidence from multiple logging and monitoring systems, along with several source repositories before debugging could even begin. Passing end-to-end tests were required to deploy services to production and thus debugging these system failures was in the critical path to getting new changes to production.

In the manual workflow, engineers moved from the test report to logs, historical patterns, and source code to classify failures and identify the likely owning team. Because these integration tests gate weekly production releases, unresolved failures could pull several teams into hours of coordination before a deployment was allowed to proceed.

The team had a clear idea of what a solution looked like: an intelligent agent that could classify and route issues automatically. What they were looking for was a structured way to go from that understanding to a working system in production, with enough structure to extend the approach to other use cases afterward.

Why Rescale chose AI-DLC

Rescale’s triage agent was a well-scoped problem with a tight timeline. The team needed a development approach that could move at that pace without skipping the architectural decisions that would determine whether the system worked in production.

The AI-Driven Development Lifecycle (AI-DLC) is an open source, AI-centric methodology for software development built around two ideas:

- AI executes, humans decide: AI generates work plans, writes code, and proposes architecture, but it surfaces decision points and asks clarifying questions rather than guessing. Humans validate and steer at each step.

- Team sessions replace async handoffs: Instead of one person building alone and requesting review later, AI-DLC uses focused group sessions: Mob Elaboration (defining requirements together) and Mob Construction (building together while AI handles implementation). Decisions happen in real time.

AI-DLC fit because the hard part wasn’t writing code. It was making sure the AI understood what the team wanted to build. In mob sessions, Rescale engineers answered the AI’s questions together and approved each plan before the AI moved on, so nothing was left to assumptions.

Solution overview

Rescale’s triage agent automates the classification and routing of nightly end-to-end test failures. It runs as a step within Rescale’s existing Argo Workflows continuous integration and continuous delivery (CI/CD) pipeline on Amazon Elastic Kubernetes Service (Amazon EKS). When a nightly Playwright suite of hundreds of test cases produces failures, the pipeline triggers the triage agent pod.

The agent performs the following steps:

- Invokes Claude Opus 4.8 on Amazon Bedrock to classify each failure into a root cause category: infrastructure issue, code bug, flaky test, or environment problem.

- Retrieves relevant historical failure patterns from a knowledge base for additional context.

- Queries pod logs through Model Context Protocol (MCP) for runtime evidence supporting the classification.

- Generates a structured remediation report with specific file references, function names, and recommended fixes for engineer review before any action is taken.

The agent completes this workflow in seconds and outputs directly into the team’s existing reporting pipeline. The following diagram shows how these components fit together:

The AI-DLC workshop experience

The engagement was structured as a five-day, in-person workshop. AWS architects guided the build while Rescale engineers worked hands-on-keyboard, co-building in real time.

Inception phase (days 1–2): From vision to validated requirements

The team started by scoping requirements, capturing the intent in a vision document and a technical environment document that grounded the work before any building began. That intent began as a single statement, “We want AI-powered test triage that classifies failures and recommends remediation”. Working collaboratively in mob format, the team answered 20+ clarification questions generated by AI. This produced a scoped requirements document with Phase 1 boundaries explicitly defined and Phase 2 items deferred. Five user stories were consolidated into four units of work:

- Knowledge base for historical failure patterns.

- AI triage agent as the core classification and remediation engine.

- Argo Workflow Integration for trigger, infrastructure, and deployment.

- Evaluation Harness for accuracy measurement and regression gating.

Human decisions drove the critical boundaries: what belongs in Phase 1 versus Phase 2, priority ordering, and deferred features like automated pull request (PR) generation.

Construction phase (days 2–4): Architecture to running code

The team defined interface contracts and data schemas, completed functional and non-functional design questions, and signed off on the architecture. Two architecture pivots emerged during the build, and the team validated each one before changing course.

First, the team planned to host the agent on Amazon Bedrock AgentCore, then moved it to Amazon EKS, where Rescale already runs its Argo Workflows CI/CD pipeline. Keeping the agent in the existing cluster meant no new runtime to operate and a direct trigger path from Argo. Second, in the early build the agent pulled log evidence from a third-party logging service through MCP. The team moved log retrieval to Amazon CloudWatch, which simplified the dependency chain. The MCP pattern stayed the same. Only the source behind it changed. Because AI-DLC keeps requirements and design decisions in shared documents, the team updated them in place and kept building without losing context.

The agent was built using the Strands SDK, supporting concurrent failure analysis with structured output in JSON and HTML, containerized on Amazon EKS.

By Day 4, the full pipeline was running in staging: Argo workflow trigger, triage agent pod, MCP log query, knowledge base retrieval, and structured triage output.

Operations phase (day 5): Evaluation and handoff

The team built a PyTest-based evaluation framework with live and stubbed test modes, establishing an initial classification-agreement baseline to measure the agent against and improve from. The agent was demoed live to Rescale’s CTO and VP of Engineering, correctly classifying both an infrastructure failure and a real code bug with specific file-level fix recommendations.

How AI-DLC scaled rigor to complexity

Not every unit of work gets the same treatment. The knowledge base needed minimal design and deployed using Terraform in hours. The Triage Agent got full rigor: architecture review, interface contracts, 12 design questions, and iterative code review. Argo Integration focused on infrastructure contracts and IAM. The Evaluation Harness used lightweight design but heavy implementation. AI-DLC matched depth to complexity automatically.

By the end of Day 5, the agent was live in staging, processing real test failures with a measured accuracy baseline and a clear path to production.

Production outcomes

The triage agent has been running in production since the workshop. It automatically classifies nightly end-to-end test failures, replacing manual first-pass triage with an automated first pass that engineers validate. For each failure, the agent generates a structured remediation report with actionable next steps rather than raw logs. The agent’s classification agreement with human triage has risen from 44% at initial deployment to 68% today. It continues to improve as the team iterates on the golden dataset and prompt engineering.

Since the triage agent entered production, the team has measured a 20% decrease in time blocking production deployment while test failures are analyzed. Because these tests gate Rescale’s weekly production releases, that reduction shows up directly in how quickly changes reach production. Engineers also spend less of the on-call rotation on repetitive classification.

Life after the workshop: From one agent to an AI-first practice

The workshop delivered one production agent, but its larger effect was on how Rescale approaches engineering work. The triage agent is part of a broader push at Rescale to deliver digital engineering capabilities for the AI era. Internally, Rescale is applying these same benefits realized from AI across its own engineering organization. AI-DLC gave Rescale’s engineers a structured, repeatable way to put that direction into practice.

Scaling AI-DLC across the organization

Rescale is evaluating an internal AI-DLC Center of Excellence, a small group that codifies best practices and helps other teams adopt the methodology independently.

The engineers who participated are already applying what they learned. One of the engineers noted that the process “will be part of my workflow going forward,” not only for building agents, but for any complex development work where AI can contribute.

Broader AI-native development

Rescale is now incorporating AI-DLC into the standard software development lifecycle for new engineering simulation products. The framework brings simulation-domain expertise, product requirements, platform architecture, validation, and operations into one structured design process. Teams resolve key questions early, capture reviewed decisions in executable specifications, and give coding agents focused work with clear acceptance criteria. This shortens the path from product concept to validated implementation while engineers remain accountable for product correctness.

The same approach is already being used in product-enabling platform work and agent systems:

- HPC simulation application catalog management.

- Container vulnerability tracking and remediation for government compliance requirements.

- Workflow-based cloud coding agents.

Conclusion

For the triage agent, the most useful output of AI-DLC was not the code itself. It was the structured path from an initial idea to a reviewed system design: clarifying questions, scoped requirements, and evaluation cri

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