Crux Labs 融资超过 6M 将企业判断转向更好的 AI Agents

供资

Crux Labs 融资超过 6M 将企业判断转向更好的 AI Agents

在 Google 上的首选来源中添加 Unite.AI

AI agent可以完成一项任务，并且仍然做出一个经验丰富的雇员会拒绝的决定。 困难的部分常常是理解不成文的规则：何时问另一个问题，何时升级，以及一个特定企业的良好结果意味着什么。

Crux Labs正在建立一个平台，将专家的判断转化为对agents企业的持续评价和改进过程。 纽约的启动已经从偷盗中兴起，超过600万美元是超额认购的融资回合，由Benchstrength和Misneach共同领导，Analog Ventures和Pareto控股公司参与其中。

公司计划利用资本扩大研究和工程建设，加快企业部署。 其中心赌注是企业需要将评价系统校准给自己的专家，并有一个反馈循环，将生产行为与agents本身的变化联系起来。

为什么 Agent 追踪只是起点

Agent可观察性帮助团队检查一个系统做了什么：信息序列，工具呼叫，以及互动背后的其他步骤。 如果出事了，那唱片是有价值的。 然而，要确定互动是否足够好，就需要有反映任务和业务的标准。

Crux认为，人工审查跟不上大规模部署的速度。 它的公告描述了各队仅采样1%的agent痕迹，使得剩余的交通量大都留在直接检查之外。 这个数字是公司对问题的描述，而不是公布的全行业调查结果。

当重大故障不同寻常时，取样可能特别有限。 agent在普通案件中可能表现得令人接受，同时在经历经验的工作人员立即承认的例外情况下挣扎。 目前的挑战是如何将这种认识转化为一种评价，这种评价可以一致地适用于更广泛的一系列互动。

对于Crux来说，机会在于了解域名的人和建造agent的工程师之间。 专题专家可以解释成功互动的外观，但这种理解需要成为衡量和改进系统的一个可重复的部分。

判断和校准评价

Crux 描述一个从公司已经使用的工具和处理程序开始的工作流程。 其agents捕捉专家判断，并用其校准定制评价。 这些评价随后支持生产监测和警报，结果为agent随后的改进提供了依据。

校准步骤很重要。 一般性评价可能会奖励一个漏掉具体业务要求的流利答案。 以专家判断为基础的评价意在承认一个听起来正确的答案与一个符合组织期望的答案之间的区别。

“Agents在实现目标方面是不可思议的，但是他们缺乏机会了解我们人类在脑海中收集的多年的热力，”称 Julie Kheyfets,Crux的共同创始人兼首席执行官。 “Crux帮助每个企业提炼这种判断，并利用它迅速改进它们的agents。”

预期成果是不断变化的评价进程，反映专家从实际互动中汲取的教训。 随着新的失败模式的出现，本组织可以将这种知识纳入对未来行为的评估中，而不是将其分散在支助票和个别审查员之间。

监测全程生产交通

Crux 称的自定义评价被优化，用于实时的监控和对100%的生产流量的警示。 这就是关于覆盖范围的说法：在整个交通流中应用评价。 这并不是说评估发现每一种可能的失败，或完全同意人类专家的看法。

这种区分很重要，因为自动化评价推出本身的计量挑战。 如果其标准不完全，审查每一次互动的系统仍然可能错失重要行为。 因此，对照领域专门知识来校正这些标准是Crux建议的核心，而不是在开始监测之前的可选步骤。

该公司还把称的评价结果推动改进运入生产。 其公告没有详细说明该流程的哪些部分会改变提示、政策、模式或其他组成部分，也没有详细说明如何处理客户批准的问题。 披露的命题是端到端的评价和改进服务，而不是特定的模式培训技术。

有企业AI经验的创始团队

Kheifets和共同创始人以及CTO Krishna Kulkarni在普林斯顿大学会面。 Kheyfets 带来在复杂产业中部署AI的十年经验，包括北美领先的Tractable AI，并担任Block翻新公司的CEO.

库尔卡尔尼此前在Meta领导品牌工程，在Checker开发AI产品，并帮助将健康化从零到2000万患者，据该公司称。 Crux正在建立一个研究人员和工程师团队，专注于评估和改进agents.

“工程组知道如何建造AI agents，主题专家知道什么是成功，“Crux将域域专业知识应用到持续的agent改进中，以便工程师能够建立真正与业务相匹配的agents。”

该公司报告与财富500家企业和保健、网络安全、金融服务和其他复杂行业的高增长企业的早期牵引。 它没有列出这些客户，也没有披露收入、部署清点或衡量通知中的改进。

具体公司评价的商业案例

Crux将自身定位为相当于企业内部一个专门的agent评价与改进团队。 商业论点是，产品和工程工作人员应花较少的时间收集审查过程并诊断一再发生的故障，而专家则有实际办法影响代表其业务的系统。

对领域专门知识的需求也出现在由专家牵头的AI培训和评价的iMerit平台上，组织专家反馈是模型改进工作流程的一部分。

对投资者来说，机会与agents进入生产后发生的事情有关。 建立agent产生了一种新的需要，即不断衡量其行为，查明其不足之处，并在不忽略组织标准的情况下加以改进。

Crux的筹资将有助于测试该过程是否能够可靠地通过客户交付，并有不同的成功定义。 它的主张是直截了当的：公司最优秀的人所持有的判断应该成为其agents评价的一部分，这些评价应该导致生产中更好的行为。 Crux实验室提供更多信息。

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。

Funding

Crux Labs Raises Over $6M to Turn Enterprise Judgment Into Better AI Agents

Add Unite.AI to your preferred sources on Google

An AI agent can complete a task and still make a decision an experienced employee would reject. The difficult part is often understanding the unwritten rules: when to ask another question, when to escalate, and what a good outcome means for a particular business.

Crux Labs is building a platform to turn that expert judgment into a continuous evaluation and improvement process for enterprise agents. The New York startup has emerged from stealth with more than $6 million in an oversubscribed funding round, co-led by Benchstrength and Misneach, with participation from Analog Ventures and Pareto Holdings.

The company plans to use the capital to expand research and engineering and accelerate enterprise deployments. Its central bet is that businesses need evaluation systems calibrated to their own experts, with a feedback loop that connects production behavior to changes in the agents themselves.

Why Agent Traces Are Only the Starting Point

Agent observability helps teams inspect what a system did: the sequence of messages, tool calls, and other steps behind an interaction. That record is valuable when something goes wrong. Deciding whether the interaction was good enough, however, requires criteria that reflect the task and the business.

Crux argues that manual review cannot keep pace with large deployments. Its announcement describes teams sampling just 1% of agent traces, leaving much of the remaining traffic outside direct inspection. That figure is the company’s description of the problem, rather than a published industry-wide survey result.

Sampling can be especially limiting when the failures that matter are unusual. An agent may behave acceptably in common cases while struggling with an exception that experienced staff recognize immediately. The challenge is converting that recognition into an evaluation that can be applied consistently across a much larger set of interactions.

For Crux, the opportunity lies between the people who know the domain and the engineers who build the agent. Subject matter experts can explain what a successful interaction looks like, but that understanding needs to become a repeatable part of how the system is measured and improved.

Capturing Judgment and Calibrating Evaluations

Crux describes a workflow that begins inside the tools and processes a company already uses. Its agents capture expert judgments and use them to calibrate custom evaluations. Those evaluations then support production monitoring and alerts, with the results feeding subsequent agent improvements.

The calibration step is important. A generic evaluation might reward a fluent answer that misses a business-specific requirement. An evaluation grounded in expert judgment is intended to recognize the distinction between an answer that sounds right and one that meets the organization’s expectations.

“Agents are incredible at attaining goals, but they lack access to the years of heuristics that we as humans have collected in our heads,” said Julie Kheyfets, Crux’s co-founder and CEO. “Crux helps every enterprise to distill this judgment and to use it to rapidly improve their agents.”

The intended outcome is a changing evaluation process that reflects what experts learn from real interactions. As new failure patterns emerge, the organization has a way to incorporate that knowledge into how it assesses future behavior, rather than leaving it scattered across support tickets and individual reviewers.

Monitoring the Full Stream of Production Traffic

Crux says its custom evaluations are optimized for real-time monitoring and alerting across 100% of production traffic. That is a claim about coverage: applying evaluation across the traffic stream. It does not mean the evaluations detect every possible failure or agree perfectly with human experts.

The distinction matters because automated evaluation introduces its own measurement challenge. A system that reviews every interaction can still miss important behavior if its criteria are incomplete. Calibrating those criteria against domain expertise is therefore central to Crux’s proposition, rather than an optional step before monitoring begins.

The company also says evaluation results drive improvements shipped into production. Its announcement does not detail which parts of that process change prompts, policies, models, or other components, or how customer approval is handled. The disclosed proposition is an end-to-end evaluation and improvement service, rather than a specified model-training technique.

A Founding Team With Enterprise AI Experience

Kheyfets and co-founder and CTO Krishna Kulkarni met at Princeton University. Kheyfets brings a decade of experience deploying AI in complex industries, including leading North America for Tractable AI and serving as CEO of Block Renovation.

Kulkarni previously led brand engineering at Meta, developed AI products at Checkr, and helped scale Healthify from zero to 20 million patients, according to the company. Crux is building a team of researchers and engineers focused on evaluating and improving agents.

“Engineering teams know how to build AI agents, and subject matter experts know what success looks like, but enterprises lack the bridge between them,” Kulkarni said. “Crux operationalizes domain expertise into continuous agent improvement so engineers can build truly business-aligned agents.”

The company reports early traction with Fortune 500 enterprises and high-growth businesses in healthcare, cybersecurity, financial services, and other complex industries. It has not named those customers or disclosed revenue, deployment counts, or measured improvements in the announcement.

The Business Case for Company-Specific Evaluation

Crux is positioning itself as the equivalent of a dedicated agent evaluation and improvement team inside an enterprise. The commercial argument is that product and engineering staff should spend less time assembling review processes and diagnosing recurring failures, while experts have a practical way to influence the systems representing their business.

The demand for domain expertise also appears in the iMerit platform for expert-led AI training and evaluation, where organizing specialist feedback is part of the model-improvement workflow.

For investors, the opportunity is tied to what happens after agents move into production. Building an agent creates a new need to measure its behavior continuously, identify where it falls short, and improve it without losing sight of the organization’s standards.

Crux’s funding will help test whether that process can be delivered reliably across customers with different definitions of success. Its proposition is straightforward: the judgment held by a company’s best people should become part of how its agents are evaluated, and those evaluations should lead to better behavior in production. More information is available at Crux Labs.

Auto-fetched and translated by AI Atlas. All rights belong to the original author.