Agentic AI

Human handoff is a key element of customer support automation—it ensures that when AI reaches its limits, a expert human can seamlessly take over. In this tutorial, we’ll implement a human handoff system for an AI-powered insurance coverage agent utilizing Parlant. You’ll discover ways to create a Streamlit-based interface that permits a human operator (Tier…

OpenAI has launched AgentKit, a cohesive platform that packages a visible Agent Builder, an embeddable ChatKit UI, and expanded Evals right into a single workflow for transport manufacturing brokers. The launch consists of Agent Builder in beta and the remaining usually accessible. What’s new? Agent Builder (beta). A visible canvas for composing multi-step, multi-agent workflows…

Do curated, tool-grounded demonstrations construct stronger software program brokers than broad piles of generic instruction information? A workforce of researchers from Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) proposes LIMI (“Less Is More for Agency”), a supervised fine-tuning technique that turns a base mannequin right into a succesful software program/analysis agent…

Building strong AI brokers differs essentially from conventional software program improvement, because it facilities on probabilistic mannequin habits slightly than deterministic code execution. This information supplies a impartial overview of methodologies for designing AI brokers which might be each dependable and adaptable, with an emphasis on creating clear boundaries, efficient behaviors, and protected interactions. What…

What if, as an alternative of re-sampling one agent, you may push Gemini-2.5 Pro to 34.1% on HLE by mixing 12–15 tool-using brokers that share notes and cease early? Google Cloud AI Research, with collaborators from MIT, Harvard, and Google DeepMind, launched TUMIX (Tool-Use Mixture)—a test-time framework that ensembles heterogeneous agent types (text-only, code, search,…

In this tutorial, we construct an superior agentic AI system that autonomously handles time collection forecasting utilizing the Darts library mixed with a light-weight HuggingFace mannequin for reasoning. We design the agent to function in a notion–reasoning–motion cycle, the place it first analyzes patterns within the knowledge, then selects an acceptable forecasting mannequin, generates predictions,…