Power Platform

DKN

Your AI Coding Agent Can Now Build and Manage Dataverse — Here’s How

The way enterprise software gets built is changing fast. Rather than manually piecing together APIs, command-line tools, and custom scripts, developers are increasingly turning to AI agents — describing what they need and letting the agent figure out the execution. But for that shift to work with platforms like Microsoft Dataverse, the platform itself needs to be something agents can actually understand and operate.

That’s exactly what Dataverse Skills delivers. Released as an open-source plugin for GitHub Copilot and Claude Code, Dataverse Skills gives AI coding agents deep, practical knowledge of Dataverse — from connecting and authenticating to building schemas, loading data, and running analytical queries. All of it driven by natural language.

What Are Dataverse Skills, Really?

At its core, Dataverse Skills is a plugin that teaches your coding agent how to work with Dataverse. It doesn’t expose a menu of commands for you to choose from. Instead, you describe your goal in plain English, and the agent decides which skills to apply, in what sequence, using which underlying tools.

Think of it as giving your AI agent a domain expert’s knowledge of Dataverse — without you needing to be that expert yourself.

The underlying engine uses the Power Platform CLI (PAC CLI) for authentication, solution management, and automation tasks, paired with the Dataverse Web API and Python SDK for data operations. But you never have to think about any of that. Natural language is the only interface you need.

Three Phases, One Unified Experience

The plugin’s capabilities are organized around three core phases of any Dataverse project:

1. Connect

The agent discovers your Dataverse environments, authenticates using PAC CLI or Azure CLI, registers the Dataverse MCP server, and sets up a consistent project structure. You don’t configure anything manually — the agent handles the entire discovery and initialization process.

2. Build

Once connected, the agent can scaffold full data models from scratch: tables, columns, choice fields, lookup relationships, many-to-many relationships, forms, and views. It picks the right tool for each task — MCP for quick reads, the Python SDK for bulk operations, and the Web API as needed — and registers every component into your solution automatically.

3. Operate

With the schema in place, the agent can load data, run cross-table analytical queries, and bulk-import records from CSV files. Need 50 realistic sample records with domain-specific content generated on the fly? One prompt is all it takes.

Seeing It in Action

Here’s a real example of what this looks like in practice. You open your terminal, install the plugin with a single command, and type:

“I’m building a logistics and inventory management system for Veloce Apparel. I need tables for Warehouses, Products, Suppliers, Shipments, and Incidents — with lookups, a many-to-many between Products and Suppliers, and a self-referential shipment routing chain (tracking a package’s journey through hub transfers). Create everything in a VeloceLogistics solution, load sample data, and show me which shipments are currently delayed or stuck in transit.”

From that single prompt, the agent autonomously:

  1. Discovers your Dataverse environment and configures MCP
  2. Creates the solution using PAC CLI
  3. Builds five tables with choice columns, lookups, and a many-to-many relationship
  4. Generates and runs a Python script to bulk-load realistic sample data
  5. Queries across tables to answer the business question

No toggling between documentation tabs. No manual CLI commands. No context switching. The agent orchestrates everything from end to end.

Works With Both GitHub Copilot and Claude Code

Development teams rarely standardize on a single AI coding tool. Some developers prefer GitHub Copilot; others work with Claude Code. Dataverse Skills was built with this reality in mind. Since the skills are written as plain Markdown files with YAML frontmatter — not compiled binaries or proprietary formats — the same plugin works identically in both environments.

Install it from the plugin marketplace for either agent and you get the same knowledge, the same safety checks, and the same results. One investment, both tools covered.

Open Source and Built to Extend

The project is MIT-licensed and openly available on GitHub. Each skill is a standalone Markdown file — readable, editable, and extensible without any compiled code. Teams can add new skills for their own Dataverse customizations, improve existing ones, or contribute bug fixes back via pull request. The architecture is deliberately approachable for anyone who wants to tailor it to their environment.

A Broader Shift in How Platforms Get Used

Dataverse Skills is more than a productivity tool — it signals a broader direction for enterprise platforms. As AI agents become a standard part of the developer workflow, the platforms they interact with need to be operable through intent, not just through traditional interfaces. Describing what you want and having it built, configured, and queryable in your environment is no longer a future concept. With Dataverse Skills, it’s available today.

Getting Started

Install the plugin with one command:

  • GitHub Copilot (VS Code): /plugin install dataverse@awesome-copilot
  • Claude Code: /plugin install dataverse@claude-plugins-official

Then describe your intent and let the agent do the rest.

Original article: Dataverse Skills: Your Coding Agent Now Speaks Dataverse by Suyash Kshirsagar, Microsoft.

DKN

Say Goodbye to Repetitive Admin Tasks: Dataverse Admin Skills Now in Public Preview

If you’ve ever spent an afternoon clicking through the Power Platform Admin Center to apply the same setting across a dozen Dataverse environments, you’ll understand the frustration. Microsoft has now addressed exactly that pain point with the public preview launch of Dataverse Admin Skills — a capability that brings natural language administration to your coding tool, whether that’s GitHub Copilot or Claude Code.

The Admin Bottleneck Problem

Picture this: your security team requests that auditing be enabled across all 20 of your Dataverse environments — today. Without automation, that means 20 separate logins, 20 sets of clicks, and 20 opportunities for human error. Alternatively, you put in a request to a developer to write a bulk script — and wait.

This is the exact gap Dataverse Admin Skills is designed to close. Instead of navigating admin consoles or waiting on scripts, you simply describe what you want in plain English, and the agent handles the rest.

How It Works

Dataverse Admin Skills operates through two complementary paths:

  • Natural Language (Agentic) Path: Using the Dataverse Skills Plugin inside GitHub Copilot or Claude Code, you describe your intent in plain English. The plugin translates your request into the appropriate PAC CLI commands, executes them against the Dataverse Web API, and gives you a clear summary of what changed. It supports multi-environment parallel execution and enforces built-in safety guardrails — including confirmation prompts before any destructive actions.
  • Direct Scripting Path: The same PAC CLI commands powering the agentic experience are available for use in Bash, PowerShell, or SDK scripts. This makes it ideal for CI/CD pipelines, runbooks, and repeatable automation workflows.

Both paths rely on PAC CLI (v2.6+, .NET Framework) and the Dataverse Web API, ensuring a consistent and trusted execution layer.

A Real Example

Say you type: “Enable AllowMCP setting on all environments starting with Preprod.”

Here’s what happens behind the scenes:

  1. The agent lists your Dataverse environments.
  2. It filters for environments matching your criteria.
  3. It asks you to confirm the target list before making any changes.
  4. It updates each environment in parallel.
  5. It presents a summary table of every change made.

One sentence. No browser tabs opened. No scripts written.

What’s Available Right Now

  • Settings Management: Read and update 37 allowlisted PPAC toggles across environments — covering MCP, audit, retention, recycle bin, search, Microsoft Fabric integration, security, and more. Works on a single environment or in bulk with parallel execution.
  • Bulk Delete: Schedule, monitor, pause, resume, and cancel bulk delete jobs. Safety is built in — confirmation prompts, FetchXML validation, and warnings for system tables help prevent accidental data loss.
  • Long-Term Retention: Enable retention on entities, set archival criteria using FetchXML, and track retention jobs. Particularly valuable for compliance scenarios where data needs to be retained but not kept in active storage.
  • Capacity Management (Coming Soon): Storage breakdowns, growth trends, capacity alerts, and archival recommendations — all accessible from your coding tool.

Getting Started in Three Steps

Step 1: Install the Plugin

  • In GitHub Copilot (VS Code): /plugin install dataverse@awesome-copilot
  • In Claude Code: /plugin install dataverse@claude-plugins-official

Step 2: Connect Your Environments

Open your coding tool and ask: “List all my Dataverse environments.” The agent will install PAC CLI if needed, authenticate you, and return your environment list. If anything is missing, it walks you through setup.

Step 3: Try It Out

Here are some prompts to get started:

  • “Enable the Microsoft Fabric integration on all production environments.”
  • “What is the recycle bin retention period for my sandbox environment?”
  • “Disable Dataverse search across all environments in the Europe region.”
  • “Cancel all system jobs that have been stuck in a waiting state since yesterday.”
  • “Set the long-term retention criteria for the custom log table to archive records older than 2 years.”

Why This Matters

Dataverse Admin Skills represents a meaningful shift in how platform administrators interact with their environments. Rather than being constrained by what a UI exposes or waiting for a developer to write automation scripts, admins can now express intent directly — and act on it at scale. The safety guardrails (allowlists, confirmation prompts, parallel execution controls) mean this power comes without sacrificing governance.

This is currently a public preview release, with Microsoft actively refining and expanding the skill set. Now is a great time to explore what it can do.

Original article: Agentic Administration: Dataverse Admin Skills now available in Public Preview by Anirudha Bakore, Microsoft.

DKN

Building Intelligent Agents with Microsoft Agent Framework: The Harness and Claw Explained

What if you could build a fully functional AI agent — one that plans, searches the web, calls your own custom tools, and remembers context across sessions — without wiring together a dozen different libraries? That’s the promise of Microsoft Agent Framework’s harness model, and this post walks through exactly how it works.

This is Part 1 of the Build Your Own Claw series. The guiding example is a personal finance assistant, and by the end you’ll have an interactive terminal agent that can look up stock prices, pull in live market news, and build a step-by-step investment plan on request.

What Is a “Harness” and What Is a “Claw”?

The terminology is deliberately visual. A claw is an agent loop — the thing that wraps a language model, connects it to tools, and keeps the conversation going. A harness is the scaffolding Microsoft Agent Framework provides around that loop: function invocation, conversation history, planning, web search, and file memory are all bundled in by default. You supply the two things that make your agent unique — its purpose (instructions) and its domain-specific tools — and the framework handles everything else.

Step 1: Connect to a Model

Every agent starts with a chat client — the component that communicates with an underlying language model. The framework treats this as a standard interface (IChatClient in .NET), so you can point it at Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Google Gemini, Ollama, or any other supported provider without changing the agent code.

For the finance assistant, the setup reads two environment variables — FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL — and authenticates using Azure’s DefaultAzureCredential. Run az login locally and it just works; in production, swap in a ManagedIdentityCredential for tighter security.

The key insight here: the harness is provider-agnostic. Switching from Foundry to OpenAI is a one-line change in the client setup — the rest of the agent stays the same.

Step 2: Wrap the Client in the Harness

This is where the framework’s value becomes obvious. A single call — AsHarnessAgent() in .NET or create_harness_agent() in Python — transforms the bare chat client into a full agent. You pass in two things:

  • Instructions — a natural language description of what the agent does and how it behaves. For the finance assistant this includes guidance on always verifying numbers with tools, citing sources, and maintaining a watchlist in a watchlist.md memory file.
  • A custom tool — a plain function (get_stock_price) that the model can invoke when it needs live data. The framework automatically generates the JSON schema from the function’s signature and parameter descriptions.

In return, the harness activates everything else automatically:

  • Web search is added as a hosted tool — ask “Any recent news on NVDA?” and it works out of the box, no extra code required.
  • Planning is enabled via a built-in TodoProvider and AgentModeProvider — so a vague request like “Review my watchlist and recommend some stocks to add” becomes a structured, step-by-step plan.
  • File memory is wired up so the agent can persist information (like your watchlist) across sessions.
  • History persistence is handled per service call.

Nothing about web search or planning required any custom implementation — those capabilities came free the moment the harness was created.

Step 3: Run the Interactive Console

Microsoft Agent Framework ships a ready-made terminal UI — the harness console — designed to be copied and adapted as a starting point for any interface (web app, chat surface, IDE extension, etc.). It outputs in colour by mode: cyan for planning, green for execution, and includes built-in commands like /todos, /mode, and /exit.

A typical interactive session with the finance assistant might look like:

  1. Switch to execute mode: /mode execute
  2. Ask for a price: “What’s the price of MSFT?” — the agent calls get_stock_price
  3. Ask for news: “Any recent news on NVDA?” — the agent uses web search
  4. Build a watchlist: “Add MSFT, NVDA and SPY to my watch list” — saved to watchlist.md
  5. Switch to plan mode: /mode plan
  6. Request analysis: “Review my watchlist and recommend some stocks to add” — the agent plans, asks clarifying questions, then executes

Sessions can be saved to disk with /session-export and restored later with /session-import, preserving conversation history, the watchlist, and all context-provider state.

How Plan Mode Actually Works

Plan mode isn’t magic — it’s structured output. When the agent is in plan mode, the console’s planning observer overrides the response format to request a JSON schema-constrained reply instead of free-form text. The model is forced into one of exactly two shapes:

  • Clarification — the model needs more information. It returns one or more questions, each optionally accompanied by suggested choices that the console renders as selectable options.
  • Approval — the model has a complete plan. It returns a single summary and the console prompts you to approve before anything executes.

This design makes agent behaviour feel deliberate and safe: the agent gathers what it needs, presents a plan, and only switches to execute mode after you sign off. The PlanningResponse schema ships with the console sample in both .NET and Python, so you can extend or reshape it to match your own UX requirements.

Toggling Features On and Off

Everything the harness enables — todos, planning modes, web search, file memory, file access, and tool approval — is on by default and individually toggleable. If your use case doesn’t need planning or web search, you disable them with a single option flag:

  • .NET: DisableTodoProvider, DisableAgentModeProvider, DisableWebSearch, DisableFileMemory, DisableFileAccess, DisableToolApproval
  • Python: disable_todo, disable_mode, disable_memory, disable_web_search

The recommended approach is to start with everything enabled and trim to taste once you understand what your agent actually needs.

You Don’t Have to Use the Full Harness

The harness is a convenience layer, not a mandatory container. All of the underlying pieces — web search (a plain tool), planning modes (a context provider), and todos (another context provider) — are individually accessible. You can cherry-pick exactly what you need and plug them into any agent architecture, even one that doesn’t use the harness at all. In .NET, the mode and todo providers live in the Microsoft.Agents.AI package; in Python, everything ships in the agent-framework package.

Try It Yourself

Both the .NET and Python runnable samples are available on GitHub:

What Comes Next

The finance assistant can now look things up, search the web, and produce structured plans. But it can’t touch your files directly, and there’s nothing yet preventing it from taking a sensitive action without asking. Part 2 of the series addresses both: granting file access, gating risky operations behind explicit approvals, and adding durable memory so the agent remembers your preferences between sessions.

Source: Microsoft Dev Blogs — Meet your agent harness and claw by Wes Steyn, Principal Software Engineer

DKN

Dataverse Long-Term Data Retention

Mastering Data verse Long-Term Data Retention: A Quick Guide

Managing database growth in Microsoft Dataverse just got easier with long-term data retention policies. Instead of permanently deleting your historical “cold” data or letting it bloat your active environment, you can securely archive it.

Here is everything you need to know to set up a data retention policy in Dataverse.

📋 Phase 1: The Prerequisites

Before you can schedule a retention policy, you need to prepare your tables and data criteria.

  • Enable the Parent Table: Go to the properties of your target table in Power Apps, expand Advanced options, and check Enable long term retention.
  • Note: Turning this on automatically enables all related child tables (like notes or tasks). It takes about 15–30 minutes to activate.
  • Create a Criteria View: Dataverse uses standard system views to determine which records get archived.
  • Best Practice: Build a dedicated view (e.g., “Cases Closed Before 2015”). Microsoft strongly recommends testing this view with a TOP N statement (like TOP 10) first to ensure your query pulls exactly what you expect before applying it to millions of rows.

⚙️ Phase 2: Setting Up the Policy

Once your tables are enabled and your view is ready, an administrator can deploy the policy:

  1. Sign into Power Apps and navigate to Retention policies on the left menu.
  2. Select New retention policy.
  3. Fill out the required details:
  • Table: Select your root parent table.
  • Name: Give your policy a clear name.
  • Criteria: Choose the Dataverse view you tested earlier.
  • Schedule & Frequency: Choose a start date and set how often it should run (Once, Daily, Weekly, Monthly, or Yearly).
  1. Hit Save.

⚠️ Critical Gotchas & Limitations

Before you hit go, keep these unique platform behaviors in mind:

  • It’s a One-Way Street: Once data is moved to long-term storage, it cannot be moved back to the active data store.
  • API Limits Apply: Running retention policies counts against your Microsoft Power Platform API request allocations.
  • Background Throttling: These policies are treated as low priority by the platform to avoid slowing down your active apps and flows. As a result, a single policy run can take 72 to 96 hours, regardless of the data volume.
  • Known Timeout Issue: If a parent table has a massive cascade chain (25+ child tables), the process might time out. The workaround is to manually enable a few child tables for retention first, then enable the parent table.
DKN

Connect to Dynamics 365 CRM Online using a client ID and secret in a C# console app

1. Prerequisites

  • Install the Dynamics 365 SDK assemblies. You can install the necessary NuGet packages, such as:
  • Microsoft.CrmSdk.CoreAssemblies
  • Microsoft.CrmSdk.XrmTooling.CoreAssembly
  • Register your app in Azure Active Directory (AAD) to retrieve the client ID, client secret, and tenant ID.

2. Code Implementation

The following code demonstrates how to authenticate and interact with Dynamics 365 CRM using the CRM SDK:

using System;
using Microsoft.Xrm.Sdk;
using Microsoft.Xrm.Tooling.Connector;

class Program
{
    static void Main(string[] args)
    {
        string clientId = "Your_Client_ID";
        string clientSecret = "Your_Client_Secret";
        string tenantId = "Your_Tenant_ID";
        string crmUrl = "https://Your_CRM_Organization.crm.dynamics.com/";

        // Create connection string
        string connectionString = $@"
            AuthType=ClientSecret;
            ClientId={clientId};
            ClientSecret={clientSecret};
            TenantId={tenantId};
            Url={crmUrl};";

        // Establish connection
        CrmServiceClient serviceClient = new CrmServiceClient(connectionString);

        if (serviceClient.IsReady)
        {
            Console.WriteLine("Connected to CRM successfully!");

            // Example: Retrieve accounts
            IOrganizationService service = serviceClient.OrganizationServiceProxy;
            QueryExpression query = new QueryExpression("account")
            {
                ColumnSet = new ColumnSet("name", "accountnumber")
            };

            EntityCollection results = service.RetrieveMultiple(query);

            foreach (var entity in results.Entities)
            {
                Console.WriteLine($"Account Name: {entity.GetAttributeValue<string>("name")}, Account Number: {entity.GetAttributeValue<string>("accountnumber")}");
            }
        }
        else
        {
            Console.WriteLine($"Failed to connect: {serviceClient.LastCrmError}");
        }
    }
}

3. Explanation

  • Authentication: The connection string uses AAD authentication with the client ID, client secret, and tenant ID.
  • Connection: CrmServiceClient establishes a connection to Dynamics 365.
  • Query: The QueryExpression retrieves data from the CRM, such as accounts in this example.

Note : This article was created with assistance from AI and there could be mistakes / error

DKN

Canvas app doesn’t show data from related records.

Solution : Refresh the Datasource and then save and publish the app . Detailed description is given below .

Description
I created a power apps with 2 text boxes where i am fetching logged in user’s Business unit and then country where he belongs .
Now , to make it clear
– i am using Dynamics 365 as a data source .
– tables involved , systemuser (User), business unit , country
i added a data source for User’s table .
Approach :
i add a global variable on start on the app where i got the user object based on the logged in user’s email address .I stored it in a variable called D365

I set the value of other 2 text boxes which should have the logged in user’s Business unit ID and country as shown below
D365User.'Business Unit'.Country.Name
D365User.'Business Unit'.Country.Country

Issue : However when i run the app it shows the data correctly for the 2 textboxes . However when i publish the app it doesn’t show any data .It showed “Getting Data ….” then blank

Solution : click “Refresh” Data source in the data source menu as shown below

then save and publish the app .

DKN

PowerApps- Enable Code Components

To enable Code components in power Apps – one need to Enable it First . the following are the steps .

Step 01 : Login to Power Platform admin portal

Step02 : Switch to your environment .

Step03 : Prodducts => Features => Enable Code Components .

Once you do the above , you should be able to import Code Components and use it in your app.