The industry consensus is that 2026 will be the year of “agentic AI.” We are rapidly moving past chatbots that simply summarize text. We are entering the era of autonomous agents that execute tasks. We expect them to book flights, diagnose system outages, manage cloud infrastructure and personalize media streams in real-time.As a technology executive overseeing platforms that serve 30 million concurrent users during massive global events like the Olympics and the Super Bowl, I have seen the unsexy reality behind the hype: Agents are incredibly fragile.Executives and VCs obsess over model benchmarks. They debate Llama 3 versus GPT-4. They focus on maximizing context window sizes. Yet they are ignoring the actual failure point. The primary reason autonomous agents fail in production is often due to data hygiene issues.In the previous era of “human-in-the-loop” analytics, data quality was a manageable nuisance. If an ETL pipeline experiences an issue, a dashboard may display an incorrect revenue number. A human analyst would spot the anomaly, flag it and fix it. The blast radius was contained.In the new world of autonomous agents, that safety net is gone.If a data pipeline drifts today, an agent doesn’t just report the wrong number. It takes the wrong action. It provisions the wrong server type. It recommends a horror movie to a user watching cartoons. It hallucinates a customer service answer based on corrupted vector embeddings.To run AI at the scale of the NFL or the Olympics, I realized that standard data cleaning is insufficient. We cannot just “monitor” data. We must legislate it.A solution to this specific problem could be in the form of a ‘data quality – creed’ framework. It functions as a ‘data constitution.’ I …
