Author name: Marcelo Acosta

There is a common failure pattern in enterprise AI agents that goes largely unnoticed until it causes real problems. Ask an agent about project deadlines, and it retrieves every meeting from the past six months. The response is technically accurate. The deadline is in there somewhere, buried beneath dozens of irrelevant status updates from March.

A team shipped a vibe-coded Lambda function last month. Claude generated it, the tests passed, and it went to production. Two days later, the function processed a payment with a negative amount. The API contract said amounts must be positive integers, but nobody wrote that down anywhere the code could check. The generated code did

A team I worked with ran their entire product on Claude 3.5 Sonnet. Every request – from simple classification to complex document analysis – hit the same model at $6 per million input tokens and $30 per million output tokens. When they implemented Bedrock’s Intelligent Prompt Routing to split traffic between Haiku and Sonnet based

Anthropic ran a public red teaming exercise against their Constitutional Classifiers system via HackerOne, offering up to $15,000 to anyone who could find a universal jailbreak. 405 invited participants spent over 3,000 hours (mean estimate: 4,720 hours) trying to answer ten targeted CBRN queries at a harmful threshold. No report succeeded. One apparent universal jailbreak

Six months ago, picking an agent framework felt like choosing a JavaScript framework in 2016 – new options every week, each claiming to be the production-ready one, none with enough real-world mileage to prove it. That changed faster than expected. LangGraph reached 1.0 in October 2025, CrewAI passed 450 million processed workflows, Amazon Bedrock AgentCore