Agentic AI

Guardrails AI has announced the general availability of Snowglobe, a breakthrough simulation engine designed to address one of the thorniest challenges in conversational AI: reliably testing AI Agents/chatbots at scale before they ever reach production. Tackling an Infinite Input Space with Simulation Evaluating AI agents—especially open-ended chatbots—has traditionally required painstaking manual scenario creation. Developers might…

In this tutorial, we explore how we can build a fully functional conversational AI agent from scratch using the Pipecat framework. We walk through setting up a Pipeline that links together custom FrameProcessor classes, one for handling user input and generating responses with a HuggingFace model, and another for formatting and displaying the conversation flow….

The landscape of software engineering automation is evolving rapidly, driven by advances in Large Language Models (LLMs). However, most approaches to training capable agents rely on proprietary models or costly teacher-based methods, leaving open-weight LLMs with limited capabilities in real-world scenarios. A team of researchers from Nebius AI and Humanoid introduced a reinforcement learning framework…

In this tutorial, we walk through building a compact but fully functional Cipher-based workflow. We start by securely capturing our Gemini API key in the Colab UI without exposing it in code. We then implement a dynamic LLM selection function that can automatically switch between OpenAI, Gemini, or Anthropic based on which API key is…

RouteLLM is a flexible framework for serving and evaluating LLM routers, designed to maximize performance while minimizing cost. Key features: Seamless integration — Acts as a drop-in replacement for the OpenAI client or runs as an OpenAI-compatible server, intelligently routing simpler queries to cheaper models. Pre-trained routers out of the box — Proven to cut…