Abstract Book of the 8th International Conference on Advanced Research in Management, Economics and Accounting
Year: 2026
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Trusted Autonomy: Designing AI Agents That Autonomously Pursue and Execute Business Opportunities While Remaining Fully Compliant
Jonathan Ocasio
ABSTRACT:
This study investigates how an autonomous Artificial Intelligence (AI) agent can identify and execute high-value business opportunities across organizational boundaries and still operate within strict policy and compliance constraints. Although multi-agent architectures are increasingly used within enterprise systems, little is known about how organizations can establish enough trust to permit an AI agent to act independently in external engagements. To address this gap, a simulation was constructed using a multi-agent design that included an Operational Agent responsible for identifying opportunities, a Compliance Agent responsible for validating every decision against organizational rules, and a Monitor Agent that captured all communication and reasoning logs. The simulation was executed thirty times, with each run requiring the AI agent to evaluate one hundred posted opportunities and autonomously determine which to pursue. Results showed that the agent maintained consistently high trust scores across all cycles, with final values ranging from 0.94 to 1.00, and pursued between ninety-six and one hundred opportunities within policy boundaries. Win rates averaged 27.88 percent and produced simulated revenue between approximately 6.4 million and 8.0 million per run, demonstrating that autonomous operation can generate meaningful business value.
These findings suggest that transparency, continuous compliance validation, and structured communication protocols enable autonomous agents to engage externally in a manner aligned with leadership intent. The study contributes a simulation-based evaluation method for
assessing trusted autonomous decision making and identifies opportunities for extending multi-agent autonomy into real-world organizational environments.
Keywords: Organization-to-Organization AI Communication, Autonomous AI Agent, Organizational Communication, Multi-Agent Collaboration, Large language Models (LLMs), Corporate AI Integration, Ethical AI Governance