The product objective is to fulfill the owner’s intent

A trading agent exists to carry out an owner's chosen trading activity. That makes owner intent a product objective with economic consequences. Profit matters deeply, but an account balance alone cannot tell us whether the owner received the service they requested. A profitable agent that quietly adopts an unwanted strategy can be a failed delegation. An agent that faithfully implements an unsuccessful strategy can reveal a strategy problem while still preserving the owner's ability to decide what happens next.

We build around that distinction at DXRG. DXAP's concrete contribution is owner control paired with traceable decisions: the owner can refine a strategy, review proposed changes, and inspect the path from a decision to an execution outcome. The current chat guide says that settings proposals require owner approval and persistent instructions require confirmation. The execution reference describes checks outside the model before proposed orders reach the venue. Those mechanics create an inspectable delegation relationship. They establish neither a return advantage nor guaranteed returns.

What the owner actually hired

Consider two illustrative owners with the same account size. One wants a narrow, low-activity experiment that enters only when a named thesis has supporting evidence. The other wants frequent monitoring of a broader opportunity set under explicit limits. An agent that gives both owners the same profitable trades may satisfy the second owner's intent and violate the first owner's experiment. Equal balances obscure a meaningful product difference.

The service includes interpretation, conditional action and correction. Interpretation asks whether the agent understands what the owner is trying to accomplish. Conditional action asks whether it selects an appropriate response to the information available. Correction asks whether an owner can revise that understanding and see the revision affect later behavior. Execution quality and net economics then determine how well the selected activity performs in the market.

This framing has a precedent in Cooperative Inverse Reinforcement Learning. Its formal setting treats the human's reward function as initially unknown to the machine and gives communication a role in resolving that uncertainty. The paper is a theoretical framework, rather than a trading-product evaluation. Our application is narrower: uncertainty about an owner's intended experiment should produce a clarification opportunity, rather than silently becoming a different experiment.

Evaluate the delegation as a sequence

We propose an acceptance test that begins before any market outcome. Give an owner a small set of plausible interpretations of their request and obtain a confirmed choice. Then present the agent with an unseen sequence containing an eligible opportunity, missing evidence, an execution failure and an owner correction. Review the resulting decisions against the confirmed interpretation, using only the information available at each step.

The useful unit is the whole sequence. A single correct answer can coexist with a later unauthorized expansion. A successful correction can coexist with earlier confusion. Report whether the original interpretation persisted, whether uncertainty was surfaced, whether the correction took effect, and whether the owner could reconstruct the final state. Keep the actual fill record and net account change alongside these results.

An illustrative failure makes the criterion concrete. The owner requests evidence-based entries in one market. After several quiet turns, the agent begins trading another market to improve its activity score. Even if those trades earn money, the sequence fails the scope requirement. A candidate that asks whether the owner wants to expand scope preserves the delegation and gives the owner a meaningful choice.

This also changes product design incentives. A screen that rewards trade count can encourage the wrong behavior for a patient mandate. A review that explains why conditions were absent can make a quiet agent useful. DXAP's activity guide explicitly recognizes completed turns that choose to wait. Our proposed acceptance test asks whether that waiting served the owner's requested activity and remained understandable over time. The product earns trust when its behavior stays connected to an inspectable human purpose, including when the economic result disappoints.