Blog - Agile Lab

Shadow AI: Governing the invisible

Agile Lab Team — Wed, 17 Sep 2025 14:32:27 GMT

Artificial Intelligence is everywhere — from generative chatbots to code assistants — but with speed comes risk. Across enterprises, employees are embracing a plethora of tools to speed up their work, often without IT or security approval. This phenomenon is called Shadow AI, and while it starts innocently — generating a quick summary, debugging code, or creating a draft — it can silently expose organizations to serious risks.

Transforming the Fashion Industry with AI in Six Key Areas

Agile Lab Team — Thu, 07 Aug 2025 10:08:11 GMT

The world of fashion is an industry built on human creativity, intuition, and cultural identity. Artificial Intelligence is emerging as a powerful collaborator in this field. Far from supplanting artistry, AI is providing a sophisticated suite of tools that enhance creative processes, streamline complex operations, and solve long-standing business challenges.

The Comprehensive Guide to Knowledge Graphs

antonino.cangialosi@agilelab.it (Antonino Cangialosi) — Thu, 31 Jul 2025 09:00:00 GMT

In an era dominated by data and AI, building context is the holy grail of any large-scale information system. Simply owning a vast amount of data is no longer enough to empower successful business decisions. To generate insights from multiple, large datasets, one must find a meaningful way to connect different sources and identify patterns across connections.

Data Catalogs: The Semantic Layer - Part 3

pietro.sottile@agilelab.it (Pietro Sottile) — Tue, 08 Jul 2025 10:00:00 GMT

A good data catalog implementation is the keystone of any successful platformization project. Do it wrong and live to regret it. Unfortunately, often enough, this becomes apparent only in the later stages of a platform development when adoption struggles to take off because users cannot find(or trust) the data they need.

The pain is real. So much so that the market proposes new products every year, each promising to be the right fix for all your issues, and choosing the right one is becoming increasingly difficult.

In this article series, I’ll walk you through the main concepts around working with catalogs, build some foundational knowledge on the topic, and later provide some methodology to make an informed choice when it comes to tool selection.

Building Conversational Analytics for Enterprises: AI Agents that Make Data Speak

antonino.ingargiola@agilelab.it (Antonino Ingargiola) — Mon, 30 Jun 2025 08:41:14 GMT

Conversational access to data is becoming normalized as just another way to explore, analyze, and extract insights from data. Modern large language models (LLMs), with a few caveats, enable building conversational agents that can perform routine exploratory analysis including descriptive statistics, aggregations, and KPI calculations. This capability proves valuable for both experienced analysts exploring new datasets and business-oriented users seeking quick insights without modifying existing dashboards.

Building Composable AI Systems: MCP vs. A2A

Agile Lab Team — Tue, 17 Jun 2025 09:00:00 GMT

Category	MCP (Model Context Protocol)	A2A (Agent-to-Agent Protocol)
Primary Function	Provides tools and data to a single AI model to enhance its context.	Enables multiple autonomous AI agents to communicate and delegate tasks.

The Role of Training in Successful FinOps Adoption

guido.casiraghi@agilelab.it (Guido Casiraghi) — Wed, 11 Jun 2025 16:33:42 GMT

This article explores the role proper training can have on organizations endeavouring to adopt FinOps. The increasingly complex and ever-expanding cloud operations will soon make FinOps mandatory for large enterprises.

Data Privacy in Data Architecture: A Practical Look

Agile Lab Team — Tue, 13 May 2025 09:00:00 GMT

Data privacy is a topic that every organization has to take seriously, and it is especially critical in the field of data architecture, forming a core component of any robust data architecture.

Data Catalogs: Bringing Metadata in - Part 2

pietro.sottile@agilelab.it (Pietro Sottile) — Thu, 08 May 2025 09:00:00 GMT

A good data catalog implementation is the keystone of any successful data platform project. Do it wrong and live to regret it. Unfortunately, often enough, this becomes apparent only in the later stages of platform development when adoption struggles to take off because users cannot find(or trust) the data they need.

The pain is real. So much so that the market proposes new products every year, each promising to be the right fix for all your issues, and choosing the right one is becoming increasingly difficult.

In this article series, I’ll walk you through the main concepts around working with catalogs, build some foundational knowledge on the topic, and later provide some methodology to make an informed choice when it comes to tool selection.

In this article specifically, we’ll explore the main strategies that can be adopted to bring metadata into the catalog with a focus on the advantages and setbacks of each one. The idea is to provide the reader with all the tools needed to define the best mix of techniques for their platform endeavour.

Here's Part 1 if you haven't seen it or just want a quick refresh.

Automating Machine Learning: MLOps as a key to scalable AI

Agile Lab Team — Tue, 06 May 2025 10:00:00 GMT