## The era of agentic chaos AI agents are rapidly evolving, moving from simple coding assistants and customer service chatbots to fundamental operational elements for businesses. This shift promises a high return on investment, but autonomy without proper alignment can lead to chaos. A mid-sized organization could manage thousands of AI agents, each making decisions that affect revenue, compliance, and customer experience. The transformation towards an agent-driven company is inevitable, but most companies are not yet ready. ## The AI reliability gap Many companies are investing heavily in AI, but without achieving the desired results. According to research by the Boston Consulting Group, 60% of companies report minimal gains in revenue and cost reduction, despite significant investments. Leading companies, on the other hand, achieve significantly better results thanks to solid data infrastructures. ## A model for agent reliability To understand where enterprise AI can fail, it is helpful to consider four critical areas: models, tools, context, and governance. * **Models:** The AI systems that interpret requests and generate responses. * **Tools:** The integration between AI and business systems. * **Context:** The information needed to understand the complete business picture. * **Governance:** The policies and controls to ensure data quality, security, and compliance. When an AI agent fails, does the problem lie in one of these areas? Is the model misunderstanding intentions? Are the tools unavailable or malfunctioning? Is the context incomplete or contradictory? Or is there no mechanism to verify that the agent did what it was supposed to do? ## The data problem The main cause of AI agent problems is often the presence of misaligned, inconsistent, or incomplete data. Companies have accumulated data debt over the decades, due to acquisitions, custom systems, and departmental tools. This data is scattered in silos that rarely agree with each other. Companies that create a unified context and solid governance can implement thousands of agents with confidence, knowing that they will work coherently and in compliance with business rules. Those who neglect this basic work will see their agents produce contradictory results, violate policies, and undermine trust. ## Leverage AI without the chaos Companies must prepare the data foundations needed to make the agent transformation work. Autonomous agents are already transforming the way work is done, but the company will only benefit if these systems operate on the basis of the same truth. Companies that generate value from AI today rely on fit-for-purpose data foundations. They understood that, in a world of agents, data is an essential infrastructure. A solid data foundation is what transforms experimentation into reliable operations.