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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Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/gursharansingh/ai-agents-in-practice-part-1-the-demo-worked-production-didnt-1o1j |
| Publication time | Mon, 18 May 2026 15:57:44 +0000 |
| Retrieval time | 2026-05-18T16:04:56.662Z |
| Last seen | 2026-05-18T16:04:56.662Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | y-FoG4wooZ6J |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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