LangSmith
6 articles tagged with this topic
Agents Are Easy to Launch, Hard to Grade — Model Companies Hit the Evaluation Wall
The biggest blocker for Agent deployment isn't model intelligence — it's not knowing if it got the answer right. Evaluating non-deterministic AI is th
AI Agents Now Rewrite Code and Databases: Sandboxes Become a Business Must-Have
AI agents now write code and change databases. OWASP shows 41% of LLM flaws come from over-permissioning. Sandboxing is governance, not engineering.
90-Point Agent Fails a Week After Launch: The Evaluation Gap Is the Real Problem
Agent hit 90 in testing, bombed with real users. Gartner: 40% of Agentic AI projects canceled by 2027, failure rate 4.2x. Bottleneck: evaluation.
AI Interviews Now Ask 'How to Handle Agent Failures'—Engineering Beats Jargon
Interviews now probe failure recovery over definitions. This signals Agent dev is in deep engineering—jargon isn't enough; you need real crash experie
Stop Trusting AI Hallucinations: A Builder's Guide to Verifiable Data Pipelines
Jepson's latest analysis exposes critical reliability gaps in modern AI stacks. Learn how to architect systems that verify outputs, enforce constraint
Stop Chasing Leaderboards: How Berkeley Exposed Flawed AI Agent Benchmarks
Berkeley researchers reveal critical data contamination in top AI benchmarks. Learn how to validate your own agent tools, avoid overfitting, and build