Designing AI-Agent Systems for Verified Digital Work: Task Discovery, Risk Assessment and Quality Control

This paper presents an applied architectural framework for designing AI-agent systems capable of identifying, evaluating and performing legitimate digital work while maintaining evidence-based controls over risk, feasibility, execution, quality assurance and financial verification. The study uses AgentEarner, an independently developed AI-agent project, as its primary software-development case study. The proposed framework consists of seven stages: opportunity discovery, legitimacy and risk assessment, feasibility evaluation, policy and execution control, deliverable production, quality assurance and acceptance, and verified outcome accounting. The paper examines practical challenges involving fragmented digital-work platforms, external authentication requirements, wallet authorization, infrastructure constraints and the distinction between advertised rewards and actual monetary earnings. It proposes mathematical models for opportunity feasibility, risk and expected economic value, together with an evaluation framework for measuring discovery accuracy, execution success, deliverable acceptance, payment realization and verified earnings. This manuscript is an exploratory applied systems study. It does not claim statistically validated performance, commercial profitability or independently verified earnings. The proposed architecture and evaluation hypotheses require further empirical validation. The author is responsible for the research, project descriptions, verification of references and final manuscript.