Not long ago, I was in a room with data leaders from three different Fortune 500 companies who were struggling to trust the data powering their AI initiatives. Despite investing heavily in AI tools and talent, they were wrestling with issues such as incorrect decisions being made on bad data. This sparked the idea for building something different at Acceldata, which aims to establish data trust as a service. The company is developing Agentic Data Management (ADM), a decentralized network of intelligent agents that operate autonomously with context, goals, and memory. These agents continuously validate and profile data in motion, detect anomalies, and enforce data policies dynamically across systems. ADM embeds integrity into the flow of data, not as a checkpoint, but as a default state, establishing trust through proactive design rather than reactive rules and siloed workflows. By building a self-improving data fabric with memory-driven learning and ethical oversight, ADM makes it possible to achieve data trust at scale, which is essential for AI systems to get it right and explain why.