Using Agents for Long-Horizon Financial Work — Aman Singh, Arca

Summary

• Arca’s $64 million raise supports an AI-native wealth firm built around human advisors. Singh wants AI to replace “all of the work around the advisor,” spanning financial planning, tax, insurance and estates. Owning the firm lets Arca standardize operations and run a proactive service around each client.
• A ten-day, $100,000 tax-bill workflow illustrates the boundary between agent analysis and human financial authority. Agents identify assets to liquidate, maintain context and summon staff; experts review plans and execute trades. Money movement and direct client communication require human involvement: “Agents prompt people more than people prompt agents.”
• Model selection follows Arca’s internal benchmarks, with Claude primary and provider dependence deliberately limited. Luna disappointed despite external benchmark claims: it was “way too slow” and costly. Tax analysis receives heavier model tiers, while background activity uses lower tiers, with routing hidden from advisors.
• Persistent client memory is the strategic layer Singh wants Arca to own. Shared state and a daily “dreaming process” connect new signals to ongoing work. He sees outsourcing memory and operational knowledge to managed agents as a control risk: “Owning your intelligence” preserves the ability to shape the business.
• Financial safety combines fixed permission boundaries with model-based verification. Singh calls restrictions “fully deterministic”; separate LLM checks examine database writes, data leakage and client artifacts. Decision models also nudge financial analysis toward verified current facts or search, addressing confident use of stale 2025 tax brackets.
• Staff feedback becomes nightly evaluation data, but engineers still approve prompt changes. Edits and cancellations carry reasons linked to agent traces; a meta-agent proposes improvements to the harness and knowledge base. Singh’s warning about autonomous prompt repair: “every little fix becomes the whole prompt.”
• The client evidence centers on discovering hidden fees, with investment performance unquantified. A prospect who believed an advisor charged $1,500 learned of additional fees and, in Singh’s account, that the advisor was not a fiduciary. Singh credits agents’ “attention to detail,” without supplying realized savings or investment returns.

Deep dive

1. Owning the firm lets Arca standardize agent work

• At AI Engineer New York 2026, MacManus introduces Arca’s $64 million raise. Singh leads its AI platform team, building the harness to run the business with AI; services span financial advice, tax, insurance and estate planning.
• Singh’s mandate is “not to replace the advisor” but “to replace all of the work around the advisor.” Clients receive human empathy and care while agents monitor markets and life circumstances; selling a business, for example, creates financial complications involving multiple partners.
• “We are the firm ourselves,” Singh stresses: Arca can standardize how work happens. His distinction from firms buying AI tools is proactive service running twenty-four seven, where “agents prompt people more than people prompt agents.”

2. Ten-day workflows depend on memory and timed human intervention

• MacManus’s tax-bill walkthrough spans ten days: a client needs $100,000, and agents analyze existing allocations to determine the most optimal assets to liquidate.
• Despite custodian APIs capable of moving money, human review gates liquidation, transfers and client communication. Experts can adjust proposed plans and execute trades. Pressed later on failed-trade recovery, Singh clarifies that Arca is not doing autonomous trading.
• Asked how resumed work stays aligned with client needs, Singh distinguishes email replies, which return to the same agent, from a new phone call, which wakes another. Shared memory records “Who owns what?”, completed work, immediate next steps and longer-term obligations, letting the new agent connect its signal to the existing tax bill.
• A daily “dreaming process” reflects on the client’s situation and updates memory. Timing matters: the person needed at step seven need not be involved earlier; agents use incoming signals to pull in the right person when required.

3. Models can change while business intelligence stays in-house

• Claude is the primary model, following earlier use of Gemini and OpenAI. Internal benchmarks govern selection: despite strong external claims for Luna, Singh found it “way too slow” and costly. Arca wants to remain model-agnostic and preserve provider optionality.
• Routing is invisible to advisors. Tax analysis receives the heaviest tier—an Opus tier model, a Sonnet tier model or a heavy GPT model—while simpler background activity uses lower tiers. One internally built harness supports these choices.
• MacManus raises managed-agent orchestration and the risk of removing humans. Singh is bullish on Anthropic and OpenAI models but reluctant to outsource memory and operational knowledge: “Owning your intelligence” preserves the ability to shape the business; handing it away risks losing control over what makes Arca distinctive.

4. Guardrails constrain agents at the edges of capability

• Singh calls safety restrictions “fully deterministic”: permission decisions sit outside the agent. Separate fast LLM calls verify every database write. Agent actions and staff edits produce logs of what changed, who changed it, where and when; “compliance cares about auditability,” and those same events can inform agents.
• Incoming emails, documents and calls are screened for prompt injection. Outbound web searches and calendar invitations face deterministic PII checks plus a consensus of small models checking for exfiltration. The principle is to “contain them at the edges of capability.”
• The agent uses a sandbox to offload computation. For data access, generated code becomes a strict format; the code is discarded and guardrails apply before the backend. Financial-plan PDFs face checks for raw account numbers and non-compliant language, such as promising returns. Singh argues tireless checking can improve compliance relative to humans.
• Financial confidence needs another check: models will happily use 2025 tax brackets for current analysis. Decision models detect actions requiring financial rigor and nudge agents toward verified knowledge or web search. Asked about decision models for failed-trade recovery, Singh says, “No, not today.”

5. Staff corrections feed improvements, while client evidence remains specific

• Staff edits and cancellations require a reason, paired with the originating agent trace. Each night, a meta-agent reviews real-world traces and suggests harness changes, including clearer tools or missing knowledge. Human engineers approve prompt changes: left alone, agents make “really nasty prompt changes,” because “every little fix becomes the whole prompt” rather than a generalizable correction.
• Experts maintain file-based operational knowledge through a first-class Knowledge tab. Agents cite those files; trace analysis surfaces incorrect information, and staff can edit it directly. Owning the firm lets Arca require feedback as part of its operating process.
• Asked for a measurable investment or tax result, Singh describes a prospect who believed an existing advisor charged $1,500. Agents reviewed documents, investments and goals, finding additional fees and, Singh says, that the advisor was not a fiduciary. The prospect had not yet become a client; Singh gives no quantified savings or investment return.