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
When financial workflows become too complex for simple parallel processing or chaining, the orchestrator-worker pattern provides dynamic coordination. Unlike fixed workflows, an orchestrator analyzes problems at runtime, breaks them into subtasks, and delegates work to specialized agents. This is the pattern that ties together everything we’ve learned - bringing intelligent coordination to financial operations.
Moving from individual prompts to production systems requires thinking architecturally. Financial workflows aren’t just chains of prompts - they’re coordinated systems where multiple specialized agents work together. Understanding how to model these workflows is essential for building reliable, maintainable financial AI systems.
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.
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.
When multiple agents work together, three challenges emerge: how do requests reach the right agent (routing), how does information flow between agents (data flow), and how do agents maintain a consistent view of the world (state coordination). In this post, I’ll explore patterns for managing these critical aspects of multi-agent systems.
A single agent can accomplish a lot, but complex real-world tasks often exceed what any one specialist can handle. Just as organizations divide work among departments, multi-agent systems distribute responsibilities across specialized agents that collaborate toward shared goals. In this post, I’ll explore how to design architectures where multiple AI agents work together effectively.
Traditional RAG (Retrieval-Augmented Generation) follows a fixed pattern: query in, documents out, response generated. But what if the agent could decide when and how to retrieve? Agentic RAG gives agents control over their own knowledge acquisition. In this post, I’ll explore this dynamic approach to retrieval, then tackle the equally important question: how do we know if our agents actually work?
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