MIT Technology Review: Scaling AI Agents Requires Trustworthy Data Pipelines

MIT Technology Review examines how data quality and provenance have become the critical bottleneck as organizations attempt to scale AI agents beyond demos into reliable production systems. The piece argues that agents fail not primarily because of model limitations but because the data they retrieve, act on, and generate is unverified, inconsistent, or poorly governed. For developers building agentic pipelines, this frames data infrastructure — RAG quality, tool output validation, memory reliability — as a first-class engineering concern rather than a secondary consideration. The article highlights emerging practices around data trustworthiness checks, structured retrieval, and audit trails as necessary components of production-grade agent systems. Teams deploying agents at scale should treat this as a checklist for architectural gaps that will cause failures in production.