Inside Arca's Long-Horizon Agents That Run the Business - Aman Singh, Arca

Arca provides financial planning, tax planning, asset management and investment advice. Its clients range from technology executives planning an early retirement to a family in New Hampshire that has made its first million dollars and wants to buy a house. Aman Singh, Arca’s AI lead, gives “AI native” a specific operational meaning: agents drive the work that determines client outcomes. Understanding that claim starts with the kind of work a financial services firm has to coordinate.

The useful distinction is long horizon versus long-running. A client goal might span ten days without requiring ten days of continuous model inference. Work starts, stops, waits for someone else, resumes after a decision and passes between people. The engineering problem is keeping the goal moving across those interruptions. 1:19

Jane’s tax bill supplies the running example. She needs cash for April 15, and the firm must work out a tax-efficient way to raise it. An advisor analyzes statements and the tax consequences of the proposed sales. A trader liquidates the assets. Those transactions take days to settle; operations staff then initiate an ACH transfer, which itself takes days to arrive. Analysis, execution and waiting all belong to the same client outcome. Repeating this across clients creates a growing coordination burden.

Arca operates the services business itself, so it can design both the software and the work agents undertake. Its approach gives agents responsibility for carrying a client outcome across the entire period: external signals keep them aware, operational context tells them how the firm handles a situation, a harness supplies tools and guardrails, persistent memory carries state, and collaboration brings people into the work. Singh describes the benefit as removing the need for someone to “babysit” a goal over days. That reduces human coordination delays; the example’s settlement and transfer waits still remain. 2:49

Jane’s email says she owes $100,000 in taxes. Background agents read it as a signal that can initiate work. The analysis also draws on client memory: eight months earlier, Jane said in a call that the firm should never touch her Apple position. That preference changes which assets the plan can consider selling. The important relationship is between the new cash requirement and an older constraint—the email alone does not contain everything needed to prepare an appropriate plan.

The system combines several distinct inputs and capabilities:

• Signals: Emails, phone calls, incoming documents, meetings, custodian events and market movements give background agents information that may advance existing work or create new work.
• Operational knowledge and client memory: The harness supplies knowledge about handling the situation, while remembered client preferences constrain the proposed action.
• Financial computation: A sandbox supports the calculations needed for the analysis.

Together these let an agent move from noticing a request to preparing a plan. The talk identifies tools and guardrails as harness components, but does not specify their enforcement rules or the financial optimization method.

Singh treats the harness as only part of the system. He sees harnesses converging toward a common shape and shifts attention to what happens around them: keeping a plan alive, noticing changes and coordinating the next participant after the initial analysis has finished.

Where does the plan become something people can inspect and act on? Agents create a space containing the proposed plan. Multiple people can chat with the agent there to understand it, and the agent can pull in someone whose participation is needed. For Jane, a person approves the plan and the outgoing email. This is a consequential limit on end-to-end ownership: the agent coordinates the outcome, while the proposed plan still requires people to execute it. 5:25

Approval creates a new waiting problem. Has Jane replied? What does her answer mean for the plan? Later, has the ACH arrived? Arca gives agents tools to register interest in events such as Jane’s response or the transfer landing. Background agents route signals to the agents interested in them. A person does not have to keep reopening the case to check whether something happened.

An event subscription cannot resolve silence by itself. If the expected signal never arrives, an agent can wake itself on a schedule to keep track of the work. Event notifications and scheduled wake-ups are separate ways to resume attention: one reacts to something happening, while the other gives the agent a chance to revisit work despite no matching event.

How can a case resume without continuous human monitoring? The diagram separates the two triggers and shows why they complement each other. The agent registers what it wants to hear about with the signal system and can independently arrange a scheduled wake-up. The schedule does not depend on Jane replying.

The next turn breaks the tidy email workflow. Jane never answers the email. Instead, someone meets her in person and learns information that affects the plan. That person enters the information into Arca’s interface, and the act of entering it becomes a signal that wakes an agent. Progress depends on capturing the new information, rather than waiting indefinitely for the original communication channel. 6:52

Internal work generates signals too. Rescheduling a calendar event or marking a task complete can notify the agent system. This extends awareness beyond incoming client communications: people’s actions inside the product can change what an agent should do next.

What changes in Jane’s case? A background agent reads the signal and notifies another agent, which updates the shared space: the cash requirement is now $80,000 instead of $100,000. The advisor’s note on the slide says Jane’s bonus arrives March 1 and she wants $20,000 of it applied to the tax bill. It demonstrates how newly captured information reaches the agent maintaining the plan, allowing that plan to change even though the expected email reply never came.

The next handoff comes from operational context. An agent determines that the person who trades these assets needs to participate, creates a task and brings that person into the work. Updated information changes the plan; knowledge of the firm’s process determines who acts next. Those are distinct responsibilities, and the system needs both to carry the goal forward.

The example now contains the main primitives of long-horizon work. Signals maintain awareness. Operational context explains what to do next. The harness performs work, persistent memory holds state across interruptions, and collaboration brings in the appropriate person. These capabilities address different failure points: an agent can know a procedure yet miss a new fact, or notice a fact yet fail to retain the unfinished work it affects.

A simple set of primitives produces a complicated operating environment. Singh describes hundreds of signals arriving, background agents communicating and tasks appearing on people’s plates. Over the preceding six months, Arca had worked on closing context gaps, improving task precision and maintaining state over long periods. The difficult question is no longer just whether an agent can analyze a tax request. It is whether the surrounding system brings in the right person at the right time. 9:01

Arca draws a practical distinction between two kinds of unfinished work:

• Work for later: Something that needs future tracking stays in the agent’s memory.
• Work doable now: Something ready for action becomes a task.

That distinction matters to the human experience. Turning every future concern into an immediate assignment would fill people’s task lists before they could usefully act. Keeping everything in memory would hide work that is ready to move.

Operating the firm gives Arca another way to improve agent behavior: change and standardize how the work happens, then encode that operational context into agent state. The software does not have to accommodate every inconsistent practice as a permanent requirement. Colleagues using the product also supply detailed reasoning that becomes evaluation material, connecting judgments from actual work to improvements in the system.

What does this coordination machinery change for an employee? When people log in, they see tasks agents have prepared after reading the relevant emails and documents. The intended handoff arrives when someone needs to act, with much of the preparation already done. Singh describes agents having completed “ninety percent of the work”; this is his rough characterization, without a defined workload or measurement method. The concrete product behavior is the prepared task list. 10:11

This reverses the usual initiation pattern: at Arca, Singh says, agents prompt people more than people prompt agents. A person does not first have to notice an email, ask for an analysis and remember the subsequent handoffs. Agents initiate and coordinate that preparation, then request human participation. Control over which work gets initiated and advanced is what connects the agent platform to the direction of the business.

The ending revisits the original trigger with a sharper judgment: Jane’s email is itself “a bit of a failure.” A firm aiming to be proactive should have prepared for her tax situation before she asked. Successfully carrying a reactive request through its handoffs still leaves the business dependent on the client noticing the need first. 11:11

Singh proposes a new business goal: every January, prepare each client’s tax situation and reach out proactively. Adding that goal to the agents’ goals would give the existing machinery a different reason to start work. The same capabilities—client context, analysis, persistent state and human coordination—would support January preparation without waiting for individual requests.

The proposed visible result is a full plan and an email ready for one-click approval on each user’s task list. People would approve the prepared outreach, moving the firm toward the new goal. This is presented as an illustration of how the architecture could support a change in business direction, rather than a measured rollout of the January program. The meaningful change is from a client email initiating one case to a recurring business objective initiating preparation across clients.

That is Arca’s ambition for an AI-native firm: “steer your business by steering your agents.” It depends on everything built earlier in the example. Goals need agents that can notice relevant events, preserve constraints, maintain unfinished work and bring people in when action is possible. The closing invitation to work on these problems follows from that operating model: the project spans both agent infrastructure and the design of the services business itself.