Boundary Conditions and Control Systems for Autonomous Multi-Agent BI Platforms

The transition from passive Business Intelligence (BI) dashboards to autonomous, agentic BI systems marks a paradigm shift in enterprise decision-making. Multi-agent Artificial Intelligence (AI) architectures increasingly execute high-frequency, operational decisions such as dynamic supply chain routing, real-time algorithmic pricing, and automated inventory balancing without real-time human-in-the-loop oversight. However, removing human intervention introduces severe boundary vulnerabilities, emergent failure modes, and systemic operational risks. This study establishes a rigorous empirical and theoretical evaluation of multi-agent autonomous decision systems under stress. Utilizing a synthetic dataset of N = 10,000 high-frequency operational decision events across variable latency, market volatility, and input noise conditions, we evaluate system behavior across four operational architectures: Rule-Based Automation, Single-Agent LLM Orchestration, Uncontrolled Multi-Agent Autonomous Execution, and Multi-Agent Execution with Dynamic Autonomous Workflow Governance (MA-AWG). Empirical results indicate that while uncontrolled multi-agent systems achieve high decision velocity, they suffer from cascading error propagation (failure rate = 18.4%) and goal-alignment drift under volatile conditions (Da = 0.47). The proposed MA-AWG framework reduces operational failure rates to 1.2%, mitigates cascading feedback loops by 89.6%, and maintains optimal decision accuracy under extreme stress scenarios. Theoretical contributions, mathematical formulations of agent drift, and managerial frameworks for safe deployment are presented.