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.
RAG and Evaluation for Financial Agents
Financial agents need two critical capabilities that set them apart from simple chatbots: the ability to ground responses in authoritative documents through intelligent retrieval, and continuous evaluation to ensure accuracy in regulated environments. Agentic RAG transforms retrieval from a passive lookup into an active reasoning loop, while long-term memory enables personalization across sessions. Together with robust evaluation frameworks, these capabilities create production-ready financial assistants.
Connecting Agents to Financial Data Sources
Agents become truly useful when they stop just reasoning and start acting - interacting with real systems like market data feeds, financial news sources, and internal databases. This shift happens through three key integration patterns: external APIs for real-time data, web search for unstructured information, and database connections for structured internal data. Mastering these patterns transforms agents from conversational tools into operational financial systems.
State and Memory for Trading Agents
LLMs are stateless by nature - each interaction is isolated, with no memory of prior prompts. But financial agents often need context to manage complex, multi-step tasks like loan approvals, insurance claims, or trading workflows. This requires two complementary mechanisms: state for tracking execution progress, and memory for maintaining conversational context across interactions.
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)
Financial Tools and Structured Outputs
Language models are strong at reasoning, but without tools, they can only talk - not act. For financial applications requiring precise calculations, real-time data access, or system integrations, tool-augmented agents are essential. Combined with structured outputs using Pydantic, we can build reliable financial systems that guarantee data format compliance.
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