Data Preparation for AI Agents

The potential of autonomous AI agents is a hot topic among executives and technologists. These agents would allow users to simply ask questions in natural language, receiving instant data-driven results. For example, someone could type a query into Slack and get an answer without needing to know how to code. However, simply tossing a powerful large language model into a corporate data warehouse without proper groundwork would turn it into a risky system.

An AI agent cannot fix underlying problems like inaccurate data, missing links between data sets, or unclear column labels. If the data infrastructure is a jumble of isolated data silos with confusing schemas, the agent will likely return incorrect answers quickly and confidently. This overconfidence in false data is often programmed into AI agents as a design choice.

The lesson learned when Runpod created a conversational data agent for querying infrastructure metrics in Slack is that the foundation of the data itself is far more important than improving the AI model itself. Minor tweaks to the model yield only small improvements. In contrast, fundamentally overhauling the data foundation has a major impact on the quality of the agent's responses.

Companies often spend large amounts of money on fine-tuning AI models and creating complex custom prompting layers. However, this is the wrong approach. Building a reliable data foundation should be the priority. To ensure an AI agent is trustworthy, organizations need to establish three core pillars for their data foundation: security, quality, and observability. These three elements provide the structure necessary for dependable AI deployment.