We deployed a LangChain agent for a client and it silently failed for two weeks. Here's what we built to make sure it never happens again.
Article summary
Quick briefing — cleaned from the original RSS feed
Six weeks ago, a LangChain agent we'd deployed for a B2B client started failing on roughly 30% of its sessions. No exceptions. No 500s. Nothing in the logs that looked wrong. The agent kept running, kept returning responses, kept looking completely healthy from the outside. We found out because the client called asking why their numbers looked off. Two weeks of silent failures. About $2,400 in wasted LLM spend before we even knew there was a problem. And the frustrating part wasn't that we…
1Key Takeaways
- Six weeks ago, a LangChain agent we'd deployed for a B2B client started failing on roughly 30% of its sessions.
- Nothing in the logs that looked wrong.
- The agent kept running, kept returning responses, kept looking completely healthy from the outside.
- We found out because the client called asking why their numbers looked off.
2AIWedia Score
8/10
High relevance — worth your attention today
Based on source trust, recency, category impact, and story depth.
3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that six weeks ago, a LangChain agent we'd deployed for a B2B client started failing on roughly 30% of its sessions.
Explore related
Browse toolsCoding AI news
Explore curated coding ai tools on AIWedia — compare, rank, and launch from our directory.
Full story on DEV — AI
Read full articleHeadlines aggregated via RSS for discovery on AIWedia. Original content © DEV — AI. We link to the source and do not republish full articles.