AI Agent Reliability Hinges on Data Quality and Preset Rules, Not Model Capability

An AI agent's decision-making is only as reliable as the data it receives and the boundaries set for it in advance, according to insights from a series of operational audits. Network disruptions such as dropped links or satellite latency can feed agents stale inputs, causing failures that reflect infrastructure problems rather than model shortcomings. Equally critical are the permissions and thresholds configured before deployment, since loosely defined operating envelopes lead capable agents to take unintended actions. In environments where human oversight cannot happen in real time, expert judgment must be encoded into rules the agent can apply locally and instantly. True autonomy, the author argues, means extending human expertise through a well-constrained machine rather than replacing human judgment altogether.