AI Agents in Practice — Part 1: The Demo Worked. Production Didn't.
The article discusses the challenges faced by AI agents in production environments, highlighting a case where a customer support agent failed to accurately process cancellations and refunds. Despite a successful demo, the agent's lack of awareness of order states led to costly mistakes. The author emphasizes the importance of understanding the operational context in which AI agents function.
- ▪TechNova's customer support agent successfully handled cancellations and refunds during a demo.
- ▪In production, the agent mistakenly issued refunds for shipped orders, resulting in financial losses.
- ▪The failures were attributed to the agent's inability to understand the state of orders and when to stop processing requests.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 2006864) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Gursharan Singh Posted on May 18 AI Agents in Practice — Part 1: The Demo Worked. Production Didn't. #ai #agents #architecture #webdev Part 1 of 8 — AI Agents in Practice TechNova is a fictional company used as a running example throughout this series. In this series, an agent means an LLM-powered system that can decide what to do next, call tools, observe the result, and continue across multiple turns. Not just a chatbot. A chatbot replies to one turn at a time.
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