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Meta has released Astryx, an open source design system that is fully customizable and built to be operated by both people and the AI agents working alongside them. It is available now in Beta. Astryx is not a new experiment. It grew inside Meta over the last eight years, where the company says it became […]
A bank deploys a new AI-powered credit decisioning model. The technology works. The model performs well. The business team is ready to move forward. Then the questions begin. Can we explain the recommendation? What data was used? Who approved the model? How do we monitor performance over time? What happens […] The post Trust first,
Trust first, scale faster: The new role of AI governance in banking Read More »
Most AI agents forget. They process a request, answer it, then drop the context. Google Cloud’s generative-ai repository now ships a sample that tackles this directly. It is the Always-On Memory Agent, a reference implementation that treats memory as a running process. Always-On Memory Agent Fundamentally, the project is a lightweight background agent that never
Introduction This tutorial starts where most agent demos stop: giving the agent persistent memory, operational context, and a place to write back what happened. An event operator does not just need an agent that can summarize a weather report or generate a generic plan. The operator needs an agent that can remember what happened at
Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph Read More »
Introduction This tutorial starts where most agent demos stop: giving the agent persistent memory, operational context, and a place to write back what happened. An event operator does not just need an agent that can summarize a weather report or generate a generic plan. The operator needs an agent that can remember what happened at
Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph Read More »
Most conversations about agentic AI focus on how autonomous these systems can become. Financial services leaders are asking a different question: Where should autonomy stop? For banks and insurers, the challenge is not simply deploying AI agents. It is determining how those systems, governance requirements and human expertise can work […] The post Human in
Human in the lead: Redefining the role of AI in financial services Read More »
Is the roof over your head about to cave in? Did you just change jobs, get married or start planning for retirement? In situations like these, most people think about their insurance. Life changes are a great time to evaluate insurance coverage (for customers and insurance companies alike), but waiting […] The post Top 5
Top 5 insurance mistakes – and how to avoid them Read More »
In this tutorial, we explore the Patter SDK by building a voice-agent workflow that simulates how an AI phone assistant behaves during real conversations. We work with a restaurant booking use case in which we define dynamic caller variables, register callable tools, apply output guardrails, simulate speech-to-text and text-to-speech behavior, and run a complete scripted
Enterprise AI initiatives rarely stall because teams lack access to capable models. Failures usually emerge below the model layer, where fragmented records, incompatible definitions, delayed pipelines, weak access controls, and unclear ownership prevent experimental systems from operating reliably across business functions. Pilot environments can conceal these weaknesses. Limited datasets are manually prepared, technical teams supervise
SpaceXAI has open-sourced Grok Build, the terminal-based AI coding agent behind its grok CLI. The source landed on GitHub today. The release covers the agent harness, TUI, CLI shell, and developer tooling under the Apache 2.0 license What is Grok Build? A harness is the scaffolding around a model. It assembles context, calls the model,
