GitHub is adapting its infrastructure and workflows to AI agents that increased code volume by 1,400 percent in 2026 by integrating AI into existing systems like CI/CD, PR review, and open-source collaboration.
A 20B search agent achieves 0.730 average curated recall across eight benchmarks by training RL on explicit state rather than integrating state management into the policy.
PaW trains environment models during policy training using the same RL rollouts, consistently improving agent performance without requiring additional simulators or inference costs.
Edamame introduces host-based runtime verification to detect code drift and misuse of autonomous AI coding agents before confidential data is exfiltrated.
Anthropic is expanding its AI-powered code security program to 150 new partners from critical infrastructure sectors, as the initial 50 partners have already identified over 10,000 critical vulnerabilities.
Small persistent adapters on shared base models can form a practical infrastructure for millions of personalized AI models when scaling, identity management, and serving requirements are systematically addressed.
The EU gains access to Anthropic’s Mythos model after weeks of restriction, but must first implement internal security measures for technical integration.
