In this tutorial, we implement a dual-agent governance system that applies Constitutional AI principles to financial operations. We demonstrate how we separate execution and oversight by pairing a Worker Agent that performs financial actions with an Auditor Agent that enforces policy, safety, and compliance. By encoding governance rules directly into a formal constitution and combining rule-based checks with AI-assisted reasoning, we can build systems that are self-reflective, auditable, and resilient to risky or non-compliant behavior in high-stakes financial workflows. Check out the FULL CODES here. !pip install -q pydantic anthropic python-dotenv import json import re from typing import List, Dict, Any,…

Qwen3-Max-Thinking is Alibaba’s new flagship reasoning model. It does not only scale parameters, it also changes how inference is done, with explicit control over thinking depth and built in tools for search, memory, and code execution. https://qwen.ai/blog?id=qwen3-max-thinking Model scale, data, and deployment Qwen3-Max-Thinking is a trillion-parameter MoE flagship LLM pretrained on 36T tokens and built on the Qwen3 family as the top tier reasoning model. The model targets long horizon reasoning and code, not only casual chat. It runs with a context window of 260k tokens, which supports repository scale code, long technical reports, and multi document analysis within a…