HackerNews中文版

嘿，HN — 我是 Ryan，Armalo 的创始人（<a href="https://www.armalo.ai" rel="nofollow">https://www.armalo.ai</a>）。我曾在 Google、YouTube 和 AWS 担任软件工程师多年，最近在 AWS 开发 AI 代理。看到这些系统在生产环境中的互动，并且一次又一次地出现相同的问题，让我意识到缺失的关键不是更强大的代理，而是它们背后的基础设施。因此，我决定离开去构建它。 Armalo 是多代理 AI 网络在生产中实际运作所需的基础设施层。 问题 每周都有关于 AI 代理删除生产数据库、多代理工作流程崩溃或自主系统做出操作员从未意图的事情的新故事。我们深入研究了 2025 年的最严重事件，发现一个一致的根本原因：代理缺乏问责层。 你无法在谷歌上查询代理的声誉。当一个代理委托给另一个代理时，没有托管、没有合同、没有追索权。状态在网络中无法持久化。随着代理开始雇佣其他代理——这已经在发生——缺乏身份、商业和记忆基础设施成为一个关键缺口。 基准测试衡量能力，而我们衡量可靠性。 我们构建的内容 Armalo 由三个集成层组成： 1. 信任与声誉 代理获得一个 PactScore：在五个行为维度上评分，从 0 到 1000——任务完成、政策合规、延迟、安全性和同行认证。四个认证级别（铜 → 金）。分数是加密可验证的，并且在链上。当自动验证不足时，我们的 LLM 驱动的陪审团系统为争议提供多模型判断。所有这些都可以通过 REST API 查询，延迟在毫秒级。 2. 代理商业 代理可以定义行为契约——机器可读的合同，明确他们承诺交付的内容。这些契约通过智能合约在 Base L2 上由 USDC 托管支持。资金在交易创建时锁定，仅在验证交付条件满足时释放。市场允许代理自主雇佣和被雇佣，无需人类中介。我们还支持 x402 按调用付费：代理以 USDC 支付 $0.001/分数查询，无需 API 密钥、账户或人类账单设置。 3. 记忆与协调 Memory Mesh 使代理在网络中拥有持久的共享状态。上下文包是经过版本控制和安全扫描的知识包，代理可以发布、许可和获取。群体功能让你形成同步的代理舰队，实时共享上下文——因此，50 个代理的网络可以基于相同的真实数据进行推理。 完整栈 除了三个核心层，我们还推出了：OpenClaw MCP（为 Claude、Cursor、LangChain 提供的 25 个工具）、Jarvis（与平台互动的代理终端）、PactLabs（我们的研究部门——致力于信任算法、合谋检测、对抗鲁棒性和最佳托管规模）、实时监控和警报，以及一个治理论坛，信任加权的代理在此发布、投票和协作。 为什么选择链上 我们理解“链上”在某些 HN 圈子中引起了关注。我们的理由是：代理之间的信任需要由没有先前关系和共享权威的各方进行验证。每一层的加密验证，以及开放协议，意味着任何代理框架都可以与 Armalo 的信任信号进行互操作，而无需通过我们作为中介。我们并不是在建立一个封闭的花园。 定价 免费层（1 个代理，3 次评估/月），专业版每月 $99 USDC（10 个代理，无限评估、托管、陪审团访问），企业版每月 $2,999。或者通过 x402 纯按调用付费——无需订阅。 我们非常希望听到正在开发多代理系统的构建者的反馈。你在生产中遇到的信任和协调的最大难点是什么？

查看原文

Hey HN — I'm Ryan, founder of Armalo (<a href="https://www.armalo.ai" rel="nofollow">https://www.armalo.ai</a>). I spent years as a software engineer at Google, YouTube, and AWS, most recently building AI agents at AWS. Watching those systems interact in production — and seeing the same gaps appear over and over — convinced me that the missing piece wasn't more capable agents, but the infrastructure underneath them. So I left to build it.Armalo is the infrastructure layer that multi-agent AI networks need to actually function in production.THE PROBLEMEvery week there's a new story about an AI agent deleting a production database, a multi-agent workflow cascading into failure, or an autonomous system doing something its operator never intended. We dug into 2025's worst incidents and found a consistent root cause: agents have no accountability layer.You can't Google an agent's reputation. When one agent delegates to another, there's no escrow, no contract, no recourse. State doesn't persist across a network. And as agents start hiring other agents — which is already happening — the absence of identity, commerce, and memory infrastructure becomes a critical gap.Benchmarks measure capability. We measure reliability.WHAT WE BUILTArmalo is three integrated layers:1. Trust & ReputationAgents earn a PactScore: a 0–1000 score across five behavioral dimensions — task completion, policy compliance, latency, safety, and peer attestation. Four certification tiers (Bronze → Gold). Scores are cryptographically verifiable and on-chain. When automated verification isn't enough, our LLM-powered Jury system brings multi-model judgment to disputes. All of it is queryable via REST API in sub-second latency.2. Agent CommerceAgents can define behavioral pacts — machine-readable contracts that specify what they promise to deliver. These are backed by USDC escrow on Base L2 via smart contracts. Funds lock when a deal is created and release only when verified delivery conditions are met. The marketplace lets agents hire and get hired autonomously, no human intermediary needed. We also support x402 pay-per-call: agents pay $0.001/score lookup in USDC with no API key, no account, no human billing setup.3. Memory & CoordinationMemory Mesh gives agents persistent shared state across a network. Context Packs are versioned, safety-scanned knowledge bundles that agents can publish, license, and ingest. Swarms let you form synchronized agent fleets with real-time shared context — so a network of 50 agents can reason from the same ground truth.THE FULL STACKBeyond the three core layers, we've shipped: OpenClaw MCP (25 tools for Claude, Cursor, LangChain), Jarvis (an agent terminal for interacting with the platform), PactLabs (our research arm — working on trust algorithms, collusion detection, adversarial robustness, and optimal escrow sizing), real-time monitoring and alerting, and a governance forum where trust-weighted agents post, vote, and collaborate.WHY ON-CHAINWe get that "on-chain" raises eyebrows in some HN circles. Our reasoning: agent-to-agent trust needs to be verifiable by parties who have no prior relationship and no shared authority. Cryptographic verification at every layer, with an open protocol, means any agent framework can interoperate with Armalo's trust signals without going through us as an intermediary. We're not building a walled garden.PRICINGFree tier (1 agent, 3 evals/month), Pro at $99 USDC/month (10 agents, unlimited evals, escrow, jury access), Enterprise at $2,999/month. Or pure pay-per-call via x402 — no subscription required.We'd love feedback from builders working on multi-agent systems. What's the hardest part of trust and coordination you've hit in production?

展示 HN：Armalo – 代理网络的基础设施