AIAI London
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Guardrails AI Introduces Snowglobe: The Simulation Engine for AI Agents and Chatbots
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…
Explainability and transparency in autonomous agents
As AI agents achieve autonomy, the necessity for explainability and transparency has by no means been extra pressing. In a current panel dialogue, 4 AI consultants (Keshavan Seshadri, Senior Machine Learning Engineer at Prudential Financial; Pankaj Agrawal, Staff Software Engineer at LinkedIn; Dan Chernoff, Data Scientist at Parallaxis; and Saradha Nagarajan, Senior Data Engineer at…
A Coding Guide to Build a Scalable Multi-Agent System with Google ADK
In this tutorial, we explore the advanced capabilities of Google’s Agent Development Kit (ADK) by building a multi-agent system equipped with specialized roles and tools. We guide you through creating agents tailored for tasks such as web research, mathematical computation, data analysis, and content creation. By integrating Google Search, asynchronous execution, and modular architecture, we…
Building a Reliable End-to-End Machine Learning Pipeline Using MLE-Agent and Ollama Locally
We start this tutorial by displaying how we are able to mix MLE-Agent with Ollama to create a completely native, API-free machine studying workflow. We arrange a reproducible atmosphere in Google Colab, generate a small artificial dataset, after which information the agent to draft a coaching script. To make it strong, we sanitize widespread errors,…
NVIDIA AI Releases Nemotron-Terminal: A Systematic Data Engineering Pipeline for Scaling LLM Terminal Agents
The race to build autonomous AI agents has hit a massive bottleneck: data. While frontier models like Claude Code and Codex CLI have demonstrated impressive proficiency in terminal environments, the training strategies and data mixtures behind them have remained closely guarded secrets. This lack of transparency has forced researchers and devs into a costly cycle…
