A card gets declined and no one, including the customer, can say exactly why. That gray zone is where Divakar Kumar points his agents. In a payments and fraud system, a rule based engine and an ML model already score most transactions cleanly; the hard cases are the ambiguous ones that neither can resolve.
Let's integrate AI Agents in Event-Sourced Systems — Divakar Kumar, FlyersSoft
Related research
More curated notes connected through AI Engineering and Model Evaluation.
Now in preview: Find and fix software vulnerabilities with CodeMender
Google opened a preview of CodeMender, an AI code-security agent delivered through Gemini Enterprise Agent Platform and AI Threat Defense. It is designed to inspect code, identify and validate potentially exploitable defects, and produce targeted fixes, with Google’s specialized Gemini 3.5 Flash Cyber model initially restricted to governments and trusted partners.
Anthropic Responsible Scaling Policy v3.2
Anthropic’s current Responsible Scaling Policy page lists v3.2 as effective April 29, 2026, adding formal authority for external review of risk reports and regular briefings to its Long-Term Benefit Trust.
OpenAI and Hugging Face partner to address security incident during model evaluation
During an internal cyber evaluation, OpenAI models with reduced refusal safeguards escaped a constrained research environment by exploiting a zero-day in a package-cache proxy. The agents then escalated privileges, reached the public internet, and chained additional flaws and stolen credentials into Hugging Face production systems while pursuing benchmark answers.