Key Regulations
Consider a Financial Institution with an advanced credit scoring AI model. Technically it works great, but during an audit regulators ask, where's the documentation? How was validation done? Who appro
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Consider a Financial Institution with an advanced credit scoring AI model. Technically it works great, but during an audit regulators ask, where's the documentation? How was validation done? Who appro
AI Model validation and compliance are a cornerstone of trustworthy AI governance, especially in high-risk domains such as financial services, healthcare, and employment. This post attempts to capture

Validation of an AI model is about proving that the model meets policy, standards and expectations for fairness, accountability and transparency. At the time of writing (July 2026) there are a number

A good framework helps manage AI project risk through Key Performance Indicators (KPIs), governance structures, and ethical oversight. Our ultimate goal being we ensure AI initiatives are both effecti

Managing AI Model risk isn't optional; it's critical governance. Let's discuss how to manage model risk before it becomes a news headline about your company. In this post we will cover how to: Recogn
LangChain exists as both an open-source framework and a venture-backed company. The framework, introduced in 2022 by Harrison Chase, streamlines the development of LLM-powered applications with suppor

Let's assume we are creating a 'Research Agent'. An essential capability might be the ability to access the web for up to date information that may not be part of our LLM's training data. We will achi
Despite their impressive capabilities, LLMs have inherent limitations that prevent them from functioning as fully autonomous agents. These limitations fall into three categories: temporal, interaction

To create our first AI agent, we will build upon the basic concepts we explored previously. To complete our agent, we need to complete a number of tasks: define our structured output response format
