Arca 如何用 AI 智能体处理长周期金融工作
Using Agents for Long-Horizon Financial Work — Aman Singh, Arca
Arca 正在构建 AI 原生财富管理公司，其 AI 负责人 Aman Singh 在 AI Engineer New York 介绍了支撑长周期金融任务的架构：自研 harness 与持久记忆，让智能体能运行跨越数天而非数小时的智能体工作流。
Arca head of AI Aman Singh joins Latent Space head of editorial Richard MacManus at AI Engineer New York, to discuss how Arca is building an AI-native wealth management firm.
Singh explains Arca's architecture, which includes a custom harness and persistent memory. He also digs into how Arca runs agentic workflows that span days, not hours.
Show notes
Arca: https://arcawealth.ai/
Chapters
00:00 Introducing Arca’s AI-native wealth management firm
01:40 How AI supports human financial advisors
02:40 Choosing models with internal benchmarks
04:01 Building a custom harness and routing tasks
05:25 Handling a $100,000 tax bill: Agents and human approvals
07:20 Agent memory across long-running tasks
09:22 Safety guardrails and prompt injection defenses
11:02 Compliance and auditability by default
12:14 “Agents prompt people more than people prompt agents”
13:46 Why Arca owns its agent infrastructure
15:05 AI-native firms versus firms using AI tools
16:09 Sandboxes and financial plan verification
18:40 Decision models and checking financial facts
19:51 Why trade execution stays with human experts
20:32 Turning staff feedback into evals and improvements
22:22 Turning operational knowledge into agent context
23:41 Client examples: Hidden fees and proactive service
来源：Latent Space · latent.space