I learned agentic AI concepts in Python - agent loops, tool calling, multi-agent coordination, production patterns. I even built my own Python assistant - MiuBot - with 10 chat channels, Temporal workflows, and multi-tenant workspaces. Python works well for most of these cases. But while building MiuBot, I kept hitting the same question: what happens when you need to serve many users concurrently over WebSocket with streaming responses? That’s when I discovered GoClaw from the NextLevelBuilder team, and it changed how I think about the problem.

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After months of studying agentic AI patterns in theory - agent loops, tool calling, multi-agent coordination - I wanted to build something real. Not another tutorial project, but an AI assistant I could actually use daily, connected to the chat platforms I already live on. That’s how MiuBot started - forked from Nanobot, then reshaped into something quite different.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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