AI Agent Projects Often Fail Due to Poor Data Prep, Not Model Limitations

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A growing number of AI agent projects run over schedule and budget not because of model failures, but because of unresolved data problems that teams fail to plan for upfront. Engineers typically scope costs around tokens, vector stores, and orchestration, while overlooking the significant effort required to clean, deduplicate, and govern the underlying data. There are two distinct data challenges: knowledge data, such as documents and FAQs, which breaks down due to format inconsistency and terminology drift, and operational data, such as customer records, which fails due to identity resolution and access authority gaps. Unresolved data issues can cause agents to return confidently wrong answers, as illustrated by a demo where the same pipeline query returns wildly different totals depending on how many flawed records are retrieved. Experts recommend teams honestly assess whether a competent human could answer the agent's intended questions from existing data before committing to any AI agent build.

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ProgrammingDEV Community ·

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