AI in ERP

Microsoft Foundry is quickly becoming the engineering‑grade backbone for building, deploying, and operationalizing AI systems at scale. Unlike traditional “chatbot builders” or UI‑only agent platforms, Foundry is designed for real developers — people who care about versioning, reproducibility, security boundaries, and clean architecture. At its core, Foundry gives you a unified place to define models, agents, tools, workflows, and evaluations, all backed by Azure’s enterprise‑grade infrastructure. But the real power shows up when you bring Foundry into Visual Studio Code, where everything becomes code‑first, scriptable, automatable, and production‑ready. In this blog, we’ll walk through the setup steps to connect and use your Microsoft Foundry LLm models in VS Code.  Prerequistes: Download and install VS Code -  https://code.visualstudio.com Set up GitHub Copilot in VS Code -  https://code.visualstudio.com/docs/copilot/setup Create a Project for Microsoft Foundry -  ...

Microsoft has just introduced Toolboxes in MS Foundry - a managed, versioned bundle of curated tools that any MCP‑compatible agent can consume through a single endpoint, removing per‑agent wiring, centralizing auth, and enabling safer rollouts What is a Toolbox in MS Foundry Toolboxes in MS Foundry lets you define a reusable set of tools once (APIs, MCP servers, connectors, skills, flows), configure authentication centrally, and expose them through a single MCP‑compatible endpoint that agents discover and call at runtime. This removes duplicated integration code and secret sprawl across agents. Read more on toolboxes here . How do Toolboxes Work? Toolboxes are a named, versioned collection of tools you manage inside Microsoft Foundry and expose through a single MCP‑compatible endpoint. Each toolbox groups tools by namespace; tools can be APIs, MCP servers, connectors, skills, or flows. At runtime an agent points to the toolbox endpoint, discovers available tools, and invokes them. Fo...

AI tools and agents are no longer optional. They’ve become embedded in how employees and developers work every day. The question is no longer if your teams will use them- it’s how safely they’ll do it. [Image reference: AI Tools: Navigating the New Professional Landscape by Vicki Reyzelman] The Risk of "Free" AI Tools Organizations must recognize the inherent dangers of allowing employees to rely on publicly available AI platforms such as ChatGPT or Google Gemini. Under the pressure of deadlines and workload, employees may inadvertently copy and paste confidential information into these tools. Proprietary strategies, source code, and intellectual property can be transmitted outside the organization’s secure environment. These disclosures occur silently, without alerts or oversight, leaving leadership unaware until the damage is irreversible. The risk of loss of control is Permanent. Once confidential information enters a public AI model, it cannot be retrieved or contained. C...