Your AI Agent Is Failing Because of Your Data Layer, Not Your Model
The reliability of AI agents often fails due to issues with the data layer rather than the model itself. Common problems include undocumented schemas, lack of data normalization, and absence of freshness tracking. Addressing these issues can significantly improve the performance of AI agents in production environments.
- ▪AI agents frequently produce unreliable outputs due to ambiguous or incorrect data context.
- ▪Undocumented schemas lead to confusion about data meanings, impacting the agent's ability to provide accurate answers.
- ▪Normalization of data from multiple sources is essential to ensure consistency in formats and values.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3850391) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ismail Haddou Posted on Jun 3 Your AI Agent Is Failing Because of Your Data Layer, Not Your Model #agents #ai #dataengineering #llm Here's a pattern I keep seeing: a team builds an AI agent, the demo works, they ship it, and within a few weeks the outputs are unreliable. Someone opens a ticket about hallucinations. Someone else suggests switching to a better model. The model isn't the issue. The data feeding the model is.
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