Google Cloud Agentspace Deep Dive
Author: Luokun Gong Introduction In today’s enterprise environment, information fragmentation has become a critical productivity bottleneck. Research shows that eighty-nine percent of employees need to switch between six or more data sources to find the information they need. Google Cloud Agentspace addresses this core challenge as an enterprise-grade intelligent agent platform that integrates scattered enterprise […]
LLM Safety and Security with Model Armor
Introduction Large Language Models (LLMs) unlock incredible new possibilities, but they also open doors to prompt injections, data leaks, and toxic outputs. If you’re building serious applications, security and compliance aren’t optional. That’s where Google Cloud Model Armor comes in. Model Armor is a guardrail service designed to protect your AI pipelines, offering rule-based filtering […]
Patterns of agentic AI
Introduction Retrieval augmented generation (RAG) marked a breakthrough for enterprise AI by helping teams surface insights and answer questions at high speed. For many, it was a launchpad: copilots and chatbots that streamlined support and reduced the time spent searching for information. However, answers alone rarely drive real business impact. Most enterprise workflows demand action […]
AI That Can Change Itself
Introduction Large language models (LLMs) are powerful but fixed; they cannot change their weights for new tasks, knowledge, or examples. We introduce Self-Adapting LLMs (SEAL), a framework that lets LLMs adjust by creating their own finetuning data and update steps. When given new input, the model makes a self-edit — this may restructure the information, […]
Introduction to AI Agents and Context Engineering
Walking through a context-driven project automation example Overview In the not-so-distant past, most “AI” systems were just advanced calculators—great for crunching numbers, but terrible at adapting or remembering what they’d done before. Fast forward to today: AI agents powered by large language models (LLMs) like OpenAI’s GPT-4 can now reason, plan, and act with an […]
From Chain-of-Thought (CoT) to Auto-CoT: Enhancing LLM Reasoning Through Prompt Engineering
Author: Yizhe Xin Introduction While large language models (LLMs) excel at straightforward tasks, they often struggle with complex multi-step reasoning. Chain-of-Thought (CoT) and its automated variant Auto-CoT address this limitation by explicitly guiding models to generate intermediate reasoning steps. This blog explores these techniques with code examples and implementation strategies. Chain-of-Thought (CoT) Explained Core Concept […]
AI Agents and Multi-Agent Systems: Building Intelligent Workflows
In today’s rapidly evolving artificial intelligence landscape, AI agents have become powerful tools for automating complex tasks and decision-making processes. This blog explores the fascinating world of AI agents, particularly focusing on multi-agent systems and how they can be implemented using Python and LangGraph. What is an AI Agent? An AI agent is a software […]