This post brings together everything we’ve learned about applying agentic AI to financial services. From role-based prompting through multi-agent coordination, we’ve covered the complete spectrum of techniques for building intelligent financial systems. Here’s the comprehensive overview with production patterns.
Coordinated Trading Systems
When multiple agents operate simultaneously on trading operations, they must share a consistent understanding of the world state. This post explores the coordination patterns that make multi-agent trading systems reliable: persistent state management, conflict resolution, and multi-agent RAG for comprehensive analysis.
Multi-Agent Architecture for Trading
When building an AI hedge fund, the most critical element is understanding the flow of agent-to-agent communication. Does one agent need to work after another? Can agents work in parallel? What is the specific purpose of each agent? Multi-agent architecture answers these questions by defining specialized agents, shared state communication, orchestration patterns, and routing strategies that together create reliable, intelligent trading systems.
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
Orchestrating Financial Operations
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.
Modeling Agentic Workflows for Finance
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.
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.