Agent 提示人，而非人提示 Agent：Arca 把财富管理公司跑在自有 Harness 上

先说结论

这是 Latent Space 在 AI Engineer New York 2026 大会现场的短访谈（采访者 Richard MacManus，嘉宾 Aman Singh，Arca 的 AI 平台团队负责人；节目官方页面核实身份，发布于 10 月 31 日）。Arca 是一家「AI 原生」财富管理公司——注意不是给理财公司卖软件的 SaaS：「我们自己就是这家公司（We are the firm ourselves）」。访谈最值得记住的一句是 Singh 的「Agent 提示人，多于人提示 Agent」：客户从不直接接触 AI，顾问也不再给 AI 派活，而是 7×24 小时运行的后台代理主动分析每个客户的处境、在正确的时间把正确的人拉进流程。支撑这个模式的是四件工程实事：跨十天的长周期记忆系统、把资金动作锁死在人手里的确定性护栏、「能力边缘收口」的多层验证，以及把员工每一次修改都变成 eval 的反馈闭环。对做 Agent 系统的读者，这是一份罕见的「AI 原生服务公司」参考架构；对观察行业的人，它演示了当 Anthropic 们开始卖金融行业方案时，应用层公司的差异化护城河还剩什么。

不是 SaaS，是公司本身：「AI 原生」的准确含义

Arca 的业务是全方位财富管理：财务建议与规划、税务规划、保险、遗产规划；客户从刚卖掉公司的企业主、筹划提前退休的大型科技公司高管，到攒钱买首套住房的小家庭（0–120s）。Singh 反复强调一个区分：「拥有 AI 工具的服务公司」要求用户去提示 Agent 干活，而「为 AI 原生而建的公司」里 Agent 主动运行整个业务（840–960s）——客户体验到的只是「这个行业从未有过的服务响应速度」，由人类顾问的共情来交付，AI 替换掉的是顾问周围的所有后台工作。因为自己就是业务方，Arca 能把工作流程本身标准化，让 Agent 以标准化方式参与工作；也能强制内部员工在每次修改或取消 Agent 结果时填写「为什么」——这两个前提对 SaaS 公司都不成立。融资方面，节目口播称「recently raised 64 million dollars」，而 Latent Space 官方文案写的是 4850 万美元、General Catalyst 领投（Index Ventures、Venrock 跟投）——两处口径不一致，如实并列。

长周期而非长运行：十天任务的记忆与信号系统

Singh 在大会演讲里给出的关键区分是 long-horizon ≠ long-running：Arca 的任务不是 Agent 连续磨几天 token，而是一个客户目标横跨十天、走走停停、多角色多决策点多交接。访谈里用「客户邮件来了一张税单、需要在组合里筹出十万美元」举例（240–480s）：分析环节——客户的钱现在都在哪、怎样清算最优化——完全由 Agent 自动完成；但任何资金划转、资产清算、以及直接联系客户的动作都被严格的人类在环护栏挡住。与托管方（custodian）有很深的 API 集成，技术上能自动执行，但策略上不允许。Agent 的产出是一份给专家审阅、可调整的执行计划。跨天恢复靠两套机制：信号系统把新邮件回复路由给已在跟进的既有 Agent，而中途插入的一通电话是全新信号、会唤醒一个新 Agent；持久记忆为每个客户维护「现在在办什么、谁负责什么、到目前为止发生了什么、未来几天与长期各在追什么」，新 Agent 靠它秒速进入状态。还有一个 Singh 称为「做梦过程」（dreaming process）的反思机制，定期回顾与客户相关的世界状态、把长周期任务的进展写回记忆——这与本博客此前介绍过的若干「反思式记忆」实践互为印证。「Agent 提示人」也由此落地：通过记忆+每日反思，Agent 判断世界状态到了哪一步、该把哪位专家在什么时机拉进来——就像人类团队里第七步的执行者不必知道第一至六步的细节。

安全与合规：确定性护栏加「能力边缘收口」

金融业务的验证体系是本期信息密度最高的部分（480–720s、960–1200s）。原则一：所有安全限制全部是确定性的，把决策权从 Agent 手里拿走——简单记账类工具 Agent 可自己执行，涉及风险的一律不行。原则二：在能力边缘收口，而不是更早——不过度约束 Agent 内部行为，但在每个出界点设卡。具体四层：每次写数据库的工具调用都伴随快速 LLM 验证；外来的邮件、文档、电话都是提示注入目标，在入口处做注入特征检测；数据离开系统时（Web 搜索、给客户 Richard 发日历邀请）先过确定性 PII 检查，再由多个小模型共识判断是否有数据外泄企图；沙箱方面 Agent 本体不在沙箱里，而是把计算卸载到沙箱——Agent 用代码查询公司数据做聚合加工，产出物（如给客户的财务规划 PDF）被翻译成严格格式、代码本身被丢弃，产出物再过确定性检查（如裸账号号码）与小模型合规审查（如是否在承诺收益）。Singh 的一个反直觉论断：这比人做合规更好，因为「AI 不知疲倦地找出最小的不合规表述」。合规侧，审计是默认能力：Agent 的每个动作产生「改了什么、谁改的、从系统哪个部分、何时」的变更日志，人类在 UI 上的动作同样入账——同一份日志反哺 Agent 循环。模型选型同样服从内部证据：Claude 是当前主力（此前历经 Gemini→OpenAI→Anthropic），按任务复杂度分层（税务分析用最重的 Opus/GPT 级，后台杂务用轻模型），顾问对此完全无感；外部基准再风光的模型（他点名 Luna「又慢又贵」）过不了内部基准就不用——模型无关、harness 自有是两条红线。也因此他对 Anthropic 新推的托管 Agent 基础设施态度明确：模型可以买，「记忆与运营知识那一层外包出去，等于把智能的所有权交出去」。对 Anthropic 进军金融服务，他的评价是「对行业是验证」，但「带 AI 工具的服务公司」与「AI 原生公司」是两个物种。

反馈闭环与知识库：把员工纠错变成 eval

改进机制同样成体系（1200–1440s）：员工的每次修改/取消带着强制的「为什么」，反馈与产生它的 Agent 轨迹配对入库；每晚一个元 Agent 跑真实轨迹，提出 harness 改进建议（误导性的工具、报错、知识库缺漏），但 prompt 变更出货前仍有人类工程师把关——因为「放任 Agent 自己改 prompt 会改得很脏：每个小修复都会膨胀成整个 prompt 重写，它们无法保持元层面、把修复泛化」。知识库是产品的一等公民：左侧 Knowledge 标签里是 Agent 正在使用的全部运营上下文，由内部专家维护，Agent 工作时会引用知识，引用出错会在轨迹分析中暴露、专家直接编辑修正。效果案例（1440–1560s）：一位尚未签约的准客户带来全套材料，Agent 读完后发现其现有顾问（收费 1500 美元还不回消息）在收他不知情的费用、且根本不是受托责任（fiduciary）身份——这种细节注意力是规模化人类团队做不到的；日常还包括主动提醒截止日、防止错过 RMD（法定最低分配额）等「对人类难以规模化、对 Agent 是默认行为」的服务。

机制与启发

三条可迁移链条：其一是「长周期」的设计范式：把多天、多角色、停停走走的业务建模为「信号→持久记忆→反思→在正确时机拉人」，而不是追求单个 Agent 连续运行——对一切有交接环节的专业服务（法律、税务、医疗随访）直接可搬。其二是护栏的位置学：确定性规则管死高危动作、LLM 验证管写操作、共识小模型管出边界、人类在环管资金与客户触点——「在能力边缘收口，而不是更早」避免了把 Agent 管死又保住了风险面；审计日志与 Agent 循环共用一份事实，是合规成本变能力成本的样本。其三是应用层护城河的重构：当模型厂开始卖行业方案（Anthropic 金融服务、托管 Agent），Arca 的回答是「拥有 harness 与记忆层=拥有智能」，外部基准不可信、行业知识库与 eval 数据才是资产——这预示 AI 应用的竞争正从「谁的模型好」转向「谁的业务能被标准化并被 Agent 参与」。观察指标：Arca 何时（是否）放开自主交易；托管 Agent 产品与自建 harness 的边界会不会被 Anthropic 的平台化挤压；以及「Agent 提示人」的交互范式能否走出财富管理这个高客单价场景。

ASR 校正表

来源：Latent Space 播客（AI Engineer NY 2026 现场访谈，约 26 分钟）转写；嘉宾与节目信息经 Latent Space 官方页面核实。文中架构细节、数字与案例均为嘉宾陈述，未独立核实；融资额两处口径已并列标注。

转写原文（修正版）

[0s - 120s] We’re actually not a SaaS company. We are we are the firm ourselves. Agents prompt people more than people prompt agents, and it is a proactive kind of service that is running for them twenty four seven. We’re here at the AI Engineer New York Conference twenty twenty six, and I’m here with Aman Singh from Arca, and Arca’s recently raised sixty four million dollars for an AI native wealth management firm. So first of all, Aman, can you tell us a bit about your role at Arca and the financial services you provide, and how you’re using AI agents to run them? Yeah, for sure. Thanks for having me. My role at Arca is I lead the AI platform team. So we’re building the underlying sort of harness and our capabilities to kind of run the business with AI, which is our ultimate goal. In terms of the services that we provide to our clients, this is a broad spectrum of financial services. So that includes sort of financial advice, financial planning for for their assets and sort of tax planning and insurance and estate planning as people get wealthier. Maybe you are someone who just sold a business, for example, and there’s lots of complexities with all the different partners, etc. Right. So we’re going to help you think through that. We also have clients that are you know really large technology executives that have are trying to figure out a real plan for retiring retiring early, right? And also small families that that are trying to buy their first home, for example. Yeah, yeah. So the the clients that you have so far, they do you have to kind of talk them through using agents and and why you’re using agents and tell them a little bit about the technology behind it. Yeah. So the role of AI at Arca is not to replace the advisor; it is to replace all of the work around the advisor.

[120s - 240s] So our clients don’t really interface with AI directly. All they experience is a level of service that has not been been been possible in this industry before, delivered through the empathy and care of a human advisor. So we’re building the machinery behind every client that’s constantly thinking about, you know, what’s what is the market doing and how does it impact Richard? You know, what what what else is going on in your life? Like, what are you? What are those the the textures of your life that impact your finances? And we’re able to be way way more proactive, way more responsive, and just deliver a better kind of level of client service using AI that does all of the the back office stuff. Okay, so let’s get into the some of the technical aspects of it. Which models are you using to power your agents, and how do you decide which model is best for for which action? Yeah, so today we’re using the frontier models from from the frontier labs, like Anthropic and OpenAI. Claude is kind of our primary model has been, but we’ve gone through several iterations of models over the years. We used to be in Gemini way back when, and then OpenAI, and then and then Anthropic. And what’s guiding all of our decisions are our benchmarks that we have internally for all of the various capabilities of our general harness that it has to express in in running our business. And so just recently, for example, despite the external benchmarks claiming great things for Luna, it didn’t really quite work out for us as as well as we had expected. It was way too slow, or and it was costing a lot. And so our internal benchmarks kind of guide our model decisions. And as a principle, we we want to be model agnostic, and and sort of not you know take a dependency on on a certain provider or a certain harness from one of these labs and kind of keep.

[240s - 360s] That that optionality open for us. Okay, so you just mentioned harnesses. So you’re using different harnesses depending on which model you’re using. No, we have we have our our own kind of built internally that is model agnostic. Okay, so you know when when your when a financial advisor is doing something like a taxation report or something like that, how does the harness decide which model to use? Is it automated? Well, yes. So we have certain tiers of models based on different complexities of tasks. So a task like assisting with a tax plan or doing a tax analysis would get our like heaviest tier of models. Inside of that tier, it is a little bit flexible. So you could use so like an Opus tier model or a Sonnet tier model or a a heavy GPT model. But based on there’s a lot of like background activity that our agents are participating in, right? That is using the a lower tier of model based on the complexity of the task. So that’s kind of how we how we make decisions on on which model serves which task. Okay, so is it the the financial advisor that’s actually making that decision while they’re using the product? No, really, they’re they’re they’re not aware of of the happening. We’ve abstracted this away from them entirely. They kind of just interact with our product as they would, and those decisions are made internally by us. Okay, so in your presentation at the conference earlier today, you talked about a tax bill example. I just wondered if you could maybe walk us through an example of adjusting a client’s portfolio, investment portfolio. You know, from identifying a need to maybe rebalance the portfolio to executing trades. Can you talk us through you know what parts of that process the agent does and and what parts the human advisor does? Right. So just like in the talk, this is a thing that will span ten days, and so agents are.

[360s - 480s] Ultimately responsible for delivering that outcome, but there are are certain parts that they do and certain parts that they do not. So in this example, when when the client emails us with this whole tax bill, there’s a there’s a piece of the analysis of like where is this client’s money allocated today and what is the most optimal way to liquidate some of that to produce a hundred thousand dollars. That is best suited for agents, right? To take on as an analysis task. Yeah. So that they do exactly that. Do they have certain limits though that they can’t you know they can’t execute a trade at correct x amount? So the analysis part is fully automated, but they’re unable to actually execute any sort of money movement or liquidation automatically. We have a very deep integration with our custodian that actually gives us APIs to be able to do that. But we have strict guardrails around human in the loop for things that would either move money or liquidate assets or even directly communicate with our with our end clients because there’s a level of service that that service and quality that we expect. So all of the like internal analysis will be done by agents, but the the they will also pull in the right person to be able to execute the trade when needed, and they’ll give it give the person a plan that they they’re able to review and kind of like adjust and yeah go from there. Yeah. Yeah. So you mentioned that these processes tend to be you know ten days or at least longer sort of running time than most sort of agent stuff these days. So when an agent kind of resumes a task maybe days later than they you know then they started it, how do you restore? How do you check that the plan still fits the client’s you know needs? Yeah. So this is an interesting interesting one to think about. Is we have tons of these signals like I described in the talk. Some are, for example, you can imagine there’s an email thread that is receiving responses and there’s chatter happening. Right. You can imagine that that any.

[480s - 600s] Additional response to that goes to the same agent that’s kind of like reading that that email thread and reacting to it, right? But a a fresh signal of like a like a phone call that was made in the middle, that is a brand new signal that isn’t attached with any other ongoing process. And so there’s like a fresh agent that wakes up to figure out what this means, right? And the way they way they understand what is happening right now is our internal memory systems. So we have we have this this persistence of for every client, what is happening right now? What are we trying to service? Who owns what? What has happened so far? What are we tracking for next? Right? What what needs to happen immediately in the next couple of days? What are we tracking for the long term? Right? Which individual is responsible for which action? All of this is being tracked by our memory systems that our agents contribute to, but also there is a a kind of really sort of dreaming process that reflects on the state of the world as it relates to Richard the client and is able to kind of like update these memory systems to to persist long horizon kind of tasks. So that agent that received a new signal quickly ramps up on what the situation is with the client, and it’ll know okay, we’re we’re actually executing a big tax bill, and we’ll be able to see okay, my signal relates to that tax bill, so let me kind of contribute to the to the state of that world. Can you talk a little bit about the the safety sort of aspects that you’ve maybe built into your product or harnessed to to make sure obviously that no money is lost along the way? Yeah, so all of our safety restrictions are are fully deterministic, and they they take the decision making off the the agent’s hands. So for example, certain tools let the let the agent kind of execute the tool itself. So if it’s like a simple record keeping kind of tool, it will execute it. We also, with every time a tool is called that writes data, we have LLM based verification running on on that action.

[600s - 720s] These like fast LLM calls that that analyze every single write to a database, so that’s another layer of verification we’re running. And then we also ingest a lot of like external information. So there’s emails coming in, documents coming in, phone calls being made, which are targets for prompt injection. So we run sort of verifications at the at at the edges that detect presence of certain you know flavors of prompt injection. And then anytime there is a there is a case where data leaves our system, for example, in like a a web search, in in the most obvious case, but also if we’re sending a calendar invite to Richard, right? That’s an external kind of data where we have both deterministic checks on presence of certain information that we know is PII, but also a consensus of small models that that ensure that there isn’t any kind of data exfil being attempted. So we have tons of different different kind of guardrails built into the harness with the principle that we want to kind of contain them at the edges of capability and not earlier than that. Yes, and and because you’re in the financial you know industry, are there certain kind of regulations that are required that you have to sort of keep to in terms of the technology that you’re using, or is that is it just more about outcomes and and the way you sort of deal with clients? Yeah, the the industry cares about outcomes, but compliance cares about auditability, right? And so our our system is is is such that auditability is by default. We have every single action that that an agent takes produces a change log of what changed, who changed it, from which part of the system, and when. And this applies not just to agents, by the way. It applies to human actors as well. So if you change something in the UI that affects a client, we’ll log that action. So it lets us both get auditability by default, but also power some of our agentic capabilities where the same thing that that we

[720s - 840s] Tracks like a UI change. Also, it could be used to like inform the agent that that happened. Right. So, auditability has been a big part of our our story, both for obviously compliance and regulation, but also for our agentic loop. Yes. In your presentation, you had a nice line: agents prompt people more than people prompt agents. So, how do the agents decide who to involve, and you know, what sort of, how much of the context that they need to provide that person in order to do what they need to do? Yeah. Another interesting one. So, this is similar to how humans work in in in in our industry. Is there is a process that we’re tracking over ten days? There’s multiple actors involved. The the actor at step seven may not need to know about this just yet, right? They’re pulled in at the right time. And so, our agents with our kind of like long memory persistence plus the daily reflection on what is happening, reflect on where the the state of the world is at the moment, and they’re able to pull in the right person at that time when it is when when there’s a signal that that implies that that person must be pulled in. And all of this is work that we have done to operationalize the context of how this industry works. But not just that, but how does Arca want it to work, and what are our principles of of how work happens in this industry? And that is another advantage of us being sort of a a a not just a SaaS company. We’re actually not a SaaS company. We are we are the firm ourselves. So we can standardize the way this work even happens, so that our agents can participate in standard ways that that work must happen. So, earlier in the conference, Anthropic did a workshop about their managed agents infrastructure, and a lot of it, even the orchestration, is becoming more automated now. So, do you foresee kind of using that type of function?

[840s - 960s] Because obviously it presents a danger, and that the human is not necessarily always in the loop anymore. So, have you had any thoughts about using that technology to automate even more of the process? I think we are very bullish on the models from from Anthropic and and the labs like OpenAI, but less so on on taking a dependency on a product like managed agents, because managed agents one of their their key things is how the models kind of persist and and contribute memory as to what is happening in the world and all the operational knowledge that they need to be need to use to to be successful. And so, letting them own that part of the business is kind of like a a risk because owning that is owning your intelligence is kind of gives you the ability to impact the business. If you if you hand off the intelligence to to another provider, you lose some of that control that you need to kind of like build something that is unique and tailored to your business to such a great degree that it can actually impact your business. And just on Anthropic, they’ve also recently released financial services, so they’re partnering with a whole bunch of different firms. Do you think what they’re doing is kind of competitive to what you’re trying to do, or do you see them as different things? Yeah, I think I think Anthropic getting into our kind of industry is is very validating for for our industry. But the way I think about it is that it is very different to be and a services firm that has access to AI tools versus a firm that was built to be AI native. The firm with AI tools requires their users to prompt the use the the agents kind of to to produce work or to do work. But here, like I said, in at Arca, agents prompt people more than people prompt agents, and it is a proactive kind of service that is running for them 24 seven and is kind of like running the business in the most standard way that we.

[960s - 1080s] That we expect at the level of service that we that we that we aspire to, which looks very different if you’re if you’re an AI AI sorry a company with AI tools. Yes. Yes. Okay. So a little bit more about on the security side. So can you explain like what in your processes runs inside a sandbox, and how do you verify like a change to a financial plan, you know, before anyone acts on it? Yeah. So the the the way we use sandboxes is that the the sandbox is one component of our harness. Our agent isn’t actually inside of the sandbox. It uses the sandbox to offload computation, and we’ve we’ve built systems to make it safe for code inside of that sandbox to access our data. So we kind of like have this thing which translates the code that it wrote into a strict format that we expect. Throw away the code, and you kind of instill guardrails on that format before it reaches your back end. So we allow the agents kind of to use code to query our data, which means that they can like aggregate it, they can munge it, they can kind of like work with it as they as they see fit to produce such a thing like a like a financial plan inside of the same sandbox. They produce this you know beautiful artifact that we we can share with clients that has like it’s a PDF of like here’s your financial plan and here’s how we’re planning to kind of like approach your life financially, and once that is produced is where we run our verifiers. So it’s a certain type of artifact that is produced from the sandbox. The moment that is produced, we run a bunch of different different verifications on it. Some deterministic that check for presence of like hey did you just put like a raw account number in there? You should not do that. So deterministically we can check that, but also fast models like like the the smaller models or Jev.

[1080s - 1200s] To analyze for presence of things that might make this artifact non-compliant. Like, for example, you really should not be like promising returns in in that artifact. So that’s another thing that we will check before it is it is kind of publishable. And the last thing I’ll say on that is that doing this is actually makes our kind of compliance better than if humans were doing it because AI is tireless at at finding even the smallest mention of a non-compliant piece of writing in there. So yeah, we’ll continue to invest in in more of that and kind of like constrain at the capability edges. Yes. Yeah, you mentioned Jev there. So you’ve started to use decision models as part of your process. What other products other than Jev are you sort of looking at? Yeah. So what we’ve noticed is when when models are working with financial data, right? When they’re answering questions that require financial knowledge, they are they’re very confident in in in the knowledge that they have. So a small example is that models will very happily use the tax brackets from 2025 to to come up with an analysis and be like, all right, that is it. But that is that is obviously incorrect. And so that is one of the ways in which we’re using decision models in in our harness is to to check when the the model is about to make a financial recommendation or or anything any action that requires financial rigor will detect and will nudge the model to kind of use our knowledge base of verified facts about the world that is true today and or use their kind of web search. So kind of makes it. This is kind of a a point about the the harness and and owning the harness that lets you do things like this that that help your your models be be accurate where it matters. And to use decision models and, for example, recovery if like a trade fails to execute, is there a decision model involved there, or is there another path?

[1200s - 1320s] to to get the agent back on track. No, not today. That is not a use case that we’re exploring today. Okay. So you know, if if a step a trade does fail, for example, how does the agent recover at the moment in your process? So so at the moment we are not doing autonomous trading with with agents. It is very much a the agents will propose that we we make trades. I see. And it is fully up to kind of our our experts to figure out if that is the right trade to be making and to execute the trade. Okay. So on the on the same sort of line of that, your slides say that every staff edit or cancellation comes with a why and come it becomes an eval. So how does that feedback get turned back into the system as a measurable improvement? Yeah, that is another one of the advantages of us being the business and not a SaaS company is that we have we have the ability to have to require them to give us feedback. And yeah, the way that we loop the feedback back into improvements is something that you would you would expect is all that feedback gets piped into a data store that that couples that feedback with the trace of the agent that produced it. Yeah. And so we’re able to kind of like take that take that trace, put it put it next to the the feedback, and every night we run this process where we take collect traces from the real world from our agents in the wild and run run a meta agent on top that suggests changes to our harness. So maybe tools that were misleading or things that were erred or there’s missing knowledge in our knowledge base, and they’ll surface this information to us, and then we’ll we’ll be able to kind of improve it. Today we still have a a human in in like a human engineer in the loop before we actually ship a prompt change. Yes. I’ve noticed that that agents will will make really

[1320s - 1440s] Nasty prompt changes. If you leave them, leave them to it. Every little fix becomes the whole prompt, and they’re they’re unable to stay meta and kind of generalize the fix into into a prompt that generalizes. So, but yeah, we have we have feedback loops from our product that we collect directly as first class product features to collect evals and turn them into improvements. Okay, and you also mentioned in your presentation about turning knowledge of how a firm works into agent contexts, which is actually something a lot of forward deployed engineers do try to do, is turn an organization’s kind of internal knowledge into actionable things for agents. What does that look like for your company technically, and and how do you sort of keep it up to date? Yeah, so we have a knowledge base of of files that is represented as as as files, and the way we keep that up to date is we we’ve handed it to our experts to curate and update. So it is a first class feature in our product. So on the left hand side, there’s a tab called Knowledge, which has the full operational context that our agents are using, and as they they work on work on things, they’re able to cite knowledge, and so it kind of like creates this this loop of like, oh, we notice that the knowledge is incorrect, and we that gets surfaced to us in our trace analyses, and because it is a a part of the product itself, people are able to go in and like edit the edit the skill directly. Okay, that’s how it kind of works. All right, okay. So you know, you mentioned you’re you’re actually offering these services to to clients through you know, I presume you have a staff of financial advisors in your firm. So can you maybe give us an example of some you know a client that you’ve had that’s had a measurable

[1440s - 1560s] Improvement in their investments or taxation or whatever through the agent system, and maybe some sort of details about what in the agent architecture kind of helped that client, you know, get such a good return. Yeah, I’ll give you a recent example. This is this person wasn’t a client yet, but they were they were at the point of becoming a client. So we had all of their their documents, their statements, their current investments, etc. And this person was already working with an advisor, and so they meet with us. They tell us that hey, I have this advisor already. They’re charging me fifteen hundred dollars for for their services, and they’re not very responsive to me. So I’m I’m chatting with you, and so our agents have kind of like you know read through all of those documents. They’ve analyzed them. They’ve analyzed what the person’s goals are that they state, right? They’ve also analyzed the statement of like who this current advisor is, right? And how much they’re they’re charging them, and they discover that that there is there are fees that this person was charging them that they weren’t aware of, and surfaces it to them, and kind of like makes them not only aware of of the fact that they’re being overcharged, but also aware that that person wasn’t even fiduciary, so they’re not obligated to act in their interests. So it is this is the sort of like attention to detail that our agents bring about in in when they’re analyzing a financial picture that that sort of would not have been possible possible if we didn’t have have these. There’s several examples of proactivity that our agents kind of like produce of like someone has a deadline coming up, or they’re at risk of missing their RMD or that kind of thing, and so this is difficult at scale for humans to keep track of, but it is the the default behavior of of our agents at at Arca. Yeah, wonderful.

[1560s - 1565s] Well, that’s probably a good point to stop at. So, thank you very much, Aman, for joining us. Thank you for having me.