Strong Data Foundations, Not Better Models, Make AI Agents Reliable

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Agentic AI systems that let employees query company data in natural language are gaining boardroom attention, but plugging a large language model into a poorly structured data warehouse produces fast, confident, and wrong answers. Runpod's experience building a Slack-based infrastructure query agent showed that improving the underlying data architecture delivered far greater gains than upgrading the model itself. Experts argue that enterprise leaders misdirect budgets by fine-tuning models instead of fixing the data foundation those models depend on. For reliable AI deployment, organizations must prioritize three core pillars: security, data quality, and observability. On the security front, granting AI agents only read-only, role-limited database access is far safer than relying on prompt instructions, which remain vulnerable to malicious injection attacks.

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Dropbox Joins Google Gemini as Connected App for File Search and Automation

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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