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
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
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)
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.
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
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.
Financial analysis often requires examining multiple dimensions simultaneously - market data, sentiment, risk factors, regulatory compliance. Rather than processing these sequentially, parallel workflows distribute work across multiple agents for speed and diverse perspectives. Combined with evaluator-optimizer patterns for quality assurance, these techniques form the backbone of production-grade financial AI systems.
Trading systems process diverse request types - market orders, limit orders, options, compliance checks - each requiring specialized handling. Two fundamental patterns enable this: prompt chaining for sequential multi-step processing, and routing for intelligent task distribution. Mastering these patterns is essential for building robust financial workflows.
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.
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