Artificial Analysis released AgentPerf, the industry's first benchmark for agentic AI. Initial results show NVIDIA Blackwell Ultra NVL72 leading, running 20x more agents per megawatt than Hopper. The benchmark measures how many concurrent agentic tasks a platform can support under real-world coding agent workloads.

Rocket Close collaborated with AWS to create Supercharger, an agentic AI solution using Strands Agents, Amazon Bedrock, and MCP tools to streamline title operations. By centralizing knowledge and automating research-heavy tasks, the solution reduced contact center calls and emails by 30%, improved title exam accuracy, and enhanced client satisfaction. This post details the technical architecture, business impact, and lessons learned.

Project Ire, Microsoft's autonomous malware-classification agent, reverse-engineered a LOTUSLITE variant that went undetected by most major EDR tools. Through behavioral analysis rather than signature matching, Ire identified the sample's malicious intent and produced a detailed function-level report consistent with Acronis's published analysis.

Andrew Singleton in 'AI Economics for Dummies' satirizes the hype and circular economics in the AI industry through a story of a crematorium and a propane company.

Google Research presents two studies on dermatology AI tools. A large-scale survey found AI assistance tripled users' accuracy in naming skin conditions, but improving next-step decisions remains challenging. A qualitative community study showed the app helped users and clinicians, with 92% of clinicians finding it helpful.

This is the third part of a series on using Lakebase for database branching, focusing on scaling to a team of fifty developers and incorporating AI agents. It covers the tier topology of long-running branches, the permission model that must be designed in advance, and the evolution of the DBA role from infrastructure management to platform engineering. The article emphasizes that the underlying methodology remains the same, but the new substrate enables practices that were previously aspirational.

Box AI built Box Agent on Deep Agents to search, analyze, and synthesize enterprise content while preserving security, permissions, and model flexibility. The parent/child agent architecture dynamically spawns sub-agents for complex tasks, and middleware handles citations, caching, and context management.