Every company sitting on a data warehouse wants the same thing: let anyone ask questions in plain English and get reliable answers. OpenAI published how they built their internal data agent, and the open-source community responded fast. Here’s a quick summary of three projects pushing this forward.
Summary: Google's AgentOps - From Prototype to Production
This post continues our coverage of Google’s agent whitepaper series, following Introduction to Agents, Agent Tools & MCP, Context Engineering, and Agent Quality & Evaluation. This fifth installment tackles the critical challenge: how do we move agents from demo to production?
Source: Prototype to Production (PDF) by Google, November 2025
Summary: Google's Agent Quality & Evaluation Framework
This post continues our coverage of Google’s agent whitepaper series, following Introduction to Agents, Agent Tools & MCP, and Context Engineering. This fourth installment tackles perhaps the most critical challenge: how do we know if an agent is actually good?
Source: Agent Quality (PDF) by Google, November 2025
Summary: Google's Context Engineering - Sessions & Memory
This is the third installment in our Agentic AI series, following Google’s Introduction to Agents and Agent Tools & MCP. While those papers covered agent architecture and tool integration, this one focuses on how agents manage context across conversations through sessions and memory.
Source: Context Engineering: Sessions & Memory (PDF) by Kimberly Milam and Antonio Gulli, Google (November 2025)
Summary: Google's Agent Tools & MCP Interoperability
Following our coverage of Google’s Introduction to Agents, this post dives into the companion whitepaper on Agent Tools and Model Context Protocol (MCP). While the first paper covered agent architecture, this one focuses on how agents interact with the world through tools and the emerging interoperability standard.
Source: Agent Tools & Interoperability with MCP (PDF) by Google
Summary: Google's Introduction to Agents
As part of our journey exploring agentic AI systems, Google’s November 2025 whitepaper “Introduction to Agents” offers valuable industry perspective on production-grade agent architecture. This post summarizes the key concepts: core architecture, Agent Ops, security patterns, and self-evolving systems. It complements what we’ve covered in our Agentic AI series.
Source: Introduction to Agents (PDF) by Google
Building Intelligent AI Systems - A Complete Guide to Agentic AI
Over the past few months, I’ve been exploring the world of agentic AI - systems where language models don’t just generate text, but reason, plan, and take action. This post serves as both an introduction and a roadmap to the complete series, sharing my thoughts on the key concepts, practical patterns, and how to get started building your own intelligent agents.
Multi-Agent RAG and Building Complete Systems
Standard RAG retrieves from a single source, but real problems often require information from multiple specialized domains. Multi-Agent RAG coordinates multiple retrieval specialists, each expert in querying specific data sources, then synthesizes their findings into coherent answers. In this final post of the series, I’ll explore Multi-Agent RAG patterns and bring together everything we’ve learned into complete, production-ready systems.