Browsing: agents

NVIDIA researchers introduced ProRL AGENT, a scalable infrastructure designed for reinforcement learning (RL) training of multi-turn LLM agents. By adopting a ‘Rollout-as-a-Service’ philosophy, the system decouples…

Google has released Gemini 3.1 Flash Live in preview for developers through the Gemini Live API in Google AI Studio. This model targets low-latency, more natural,…

This post is a collaboration between AWS and Pipecat. Deploying intelligent voice agents that maintain natural, human-like conversations requires streaming to users where they are, across…

The current state of AI agent development is characterized by significant architectural fragmentation. Software devs building autonomous systems must generally commit to one of several competing…

Starting on Monday, the Trump administration will be sending ICE agents to airports across the country, Trump’s border czar Tom Homan announced on Sunday. Funding for…

Image by Editor   # Introduction  If you have built AI agents that work perfectly in your notebook but collapse the moment they hit production, you are…

Sponsored Content      In this Abacus AI review, we explore how ChatLLM, the AI assistant built on the Abacus ecosystem, allows users to experiment with…

Moving AI agents from prototypes to production surfaces a challenge that traditional testing is unable to address. Agents are flexible, adaptive, and context-aware by design, but…

The deployment of autonomous AI agents—systems capable of using tools and executing code—presents a unique security challenge. While standard LLM applications are restricted to text-based interactions,…

Most LLM agents work well for short tool-calling loops but start to break down when the task becomes multi-step, stateful, and artifact-heavy. LangChain’s Deep Agents is…