7.4 Proposing Microsoft Power Platform AI Features & the AI Hub

Microsoft Certified: Agentic AI Business Solutions Architect Expert AB-100

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1 Chapter 1: Requirements Analysis & Grounding Data

1.1 Assessing Agents for Task Automation, Analytics & Decision-Making 1.2 Grounding Data Evaluation: Accuracy, Relevance, Timeliness, Cleanliness & Availability 1.3 Structuring Enterprise Business Data for AI System Interoperability

2 Chapter 2: AI Strategy, Cloud Adoption Framework & CoE

2.1 Cloud Adoption Framework (CAF) for Azure AI Strategy & Roadmap 2.2 Multi-Agent Architecture: Microsoft 365 Copilot, Copilot Studio & Foundry 2.3 Microsoft AI Center of Excellence (CoE) & Solution Governance 2.4 Developing Use Cases for Prebuilt Agents & the Microsoft Agent Catalog 2.5 Solution Rules & Constraints for Copilot Studio, Microsoft Foundry & Foundry Tools

3 Chapter 3: Model Selection, Prompt Engineering & Dynamics AI

3.1 Model Selection Strategy, Custom Models & Small Language Models (SLMs) 3.2 Enterprise Prompt Engineering Guidelines & Prompt Libraries 3.3 Cross-App AI Strategy for Multi-App Dynamics 365 Solutions

4 Chapter 4: ROI, TCO, Build vs Buy & Model Routing

4.1 ROI Criteria & Total Cost of Ownership (TCO) for AI Solutions 4.2 Comprehensive Financial ROI Analysis for Business Processes 4.3 Build vs Buy vs Extend Architectural Decision Framework 4.4 Dynamic Model Router Architecture & Cost-Performance Optimization

5 Chapter 5: Dynamics 365 AI & Contact Center Design

5.1 Business Terms & Copilot Customization in Customer Experience and Service 5.2 Connectors & Data Flow Design for Copilot in Dynamics 365 Sales 5.3 Agent Integration with Dynamics 365 Contact Center Channels

6 Chapter 6: Agent Archetypes & Copilot Studio Core

6.1 Designing Task Agents, Autonomous Agents & Prompt-and-Response Agents 6.2 Copilot Studio Topic Design, Trigger Phrases & Graceful Fallbacks 6.3 Orchestration Selection: Standard NLP vs CLU vs Generative AI Orchestration

7 Chapter 7: Copilot Studio Advanced Actions & Power Platform

7.1 Designing Agent Flows & Prompt Actions in Copilot Studio 7.2 Power Apps Canvas App Integration, Generative Pages & Agent Feeds 7.3 Power Platform Well-Architected Framework for AI Workloads 7.4 Proposing Microsoft Power Platform AI Features & the AI Hub

8 Chapter 8: Agent Extensibility, MCP & Computer Use

8.1 Microsoft Foundry Custom Models & Foundry Tools Selection 8.2 Agent Extensibility via Model Context Protocol (MCP) in Copilot Studio 8.3 Computer Use Automation & Web UI Interactions in Copilot Studio

9 Chapter 9: Advanced Behaviors & Prebuilt App Orchestration

9.1 Agent Behaviors: Multi-Step Reasoning, Voice Mode & M365 Agent Optimization 9.2 Orchestrating Prebuilt AI in Dynamics 365 F&O and M365 Copilot for Sales/Service 9.3 Orchestrating Prebuilt Copilot AI Features in Dynamics 365 Customer Experience & Service 9.4 Proposing Microsoft 365 Agents for Business Scenarios 9.5 Adding Knowledge Sources to In-App Help & Guidance in Dynamics 365 Finance & Supply Chain

10 Chapter 10: Telemetry, Monitoring & Agent Tuning

10.1 Monitoring Architectures: Azure Monitor, Application Insights & Studio Analytics 10.2 Telemetry Data Interpretation, Performance Metrics & Latency Tuning 10.3 User Feedback Analysis, Backlog Prioritization & Continuous Improvement

11 Chapter 11: Testing Strategy & Model Validation

11.1 Agent Testing Frameworks, Quality Metrics & Groundedness Benchmarks 11.2 Custom AI Model Validation, Safety Criteria & Regression Testing 11.3 Copilot-Assisted Test Case Generation & Cross-Dynamics End-to-End Testing 11.4 Validating Effective Copilot Prompt Practices

12 Chapter 12: ALM for Grounding Data & Copilot Studio Agents

12.1 ALM for Grounding Data, Vector Indexes & Knowledge Synchronization 12.2 ALM for Copilot Studio Agents, Connectors & Solution Packaging 12.3 Environment Strategy & Automated Deployment Pipelines for Studio Agents

13 Chapter 13: ALM for Foundry Agents & Dynamics 365 AI

13.1 ALM for Microsoft Foundry Agents Service & Model Registries 13.2 ALM for Custom AI Models, Fine-Tuning Artifacts & Deployment Slots 13.3 ALM for Dynamics 365 AI in Finance, Supply Chain & Customer Engagement

14 Chapter 14: Agent Identity, Security & Access Controls

14.1 Agent Identity Architecture, Microsoft Entra ID & Authentication Flows 14.2 Access Controls, Role-Based Security & Permissions on Grounding Data 14.3 Secure Data Processing & Grounding Data Isolation

15 Chapter 15: Model Security & Threat Mitigation

15.1 Model Security Architecture & Defense Against Vulnerabilities 15.2 Mitigating Prompt Manipulation, Jailbreaks & Indirect Prompt Injections 15.3 Azure AI Content Safety, Prompt Shields & Guardrails

16 Chapter 16: Responsible AI, Governance, Residency & Audit

16.1 Microsoft Responsible AI Standard: Principles, Assessment & Implementation 16.2 Data Residency, Sovereign Boundaries & Cross-Border Compliance 16.3 Comprehensive Audit Trails, Model Decision Logging & Microsoft Purview 16.4 Designing Governance for Agents: Environment Strategy, Data Policies & Agent Inventory

7.4 Proposing Microsoft Power Platform AI Features & the AI Hub

Key Takeaways Explain This Topic AI

The AI hub is reached in Power Apps or Power Automate through More then AI hub then AI models, and consolidates AI models, prompts, agents and consumption telemetry into a single governance surface bounded by the Dataverse environment.

AI Builder splits models by build type: prebuilt models such as invoice processing, receipt processing, contract processing, ID reader and sentiment analysis are ready to use, while document processing, prediction and object detection are custom models the maker builds, trains and publishes.

An AI prompt is a governed Dataverse artefact consumable from Power Apps, Power Automate cloud flows, Copilot Studio agent flows and Copilot Studio agents, which prevents the same instruction being duplicated across three surfaces.

AI Builder activity monitoring aggregates runs from Power Automate, Power Apps and Copilot Studio, but visibility is role-scoped: only System Administrator and System Customizer see all activity, while Basic User and Environment Maker see only their own.

Activity data persists in the AI Event Dataverse table even after the model, flow and app are deleted, and contains text inputs for text scenarios, so a recurring bulk-delete job configured through Power Platform admin center data management is a retention requirement rather than an optimisation.

Last updated: September 2026

Microsoft Power Platform AI Features & the AI Hub

Quick Answer: The AI hub is the consolidated AI surface inside the Power Apps and Power Automate maker portals, reached from the left navigation through ... More > AI hub , where makers create and manage AI models , prompts , and agents , and where the Monitor activity area exposes AI Builder activity for consumption and governance. Underneath it sits AI Builder , which supplies two classes of model: prebuilt models that are ready to use for scenarios common across businesses (business card reader, text recognition, receipt processing, invoice processing, ID reader, contract processing, key phrase extraction, language detection, sentiment analysis, text translation, text generation), and custom models that the maker builds, trains, and publishes on business-specific data (document processing, prediction, object detection, plus customisable category classification and entity extraction). Activity data is written to the AI Event Dataverse table ( msdyn_aievent ), which persists even after the model, flow, or app is deleted — making it both a governance asset and a data-retention obligation. Do not confuse this with Microsoft Purview AI Hub , which is a security and compliance surface for AI risk, not a Power Platform maker surface.

Architects arriving from Azure frequently under-use the Power Platform AI layer. They reach for a Foundry model deployment and a custom orchestration when the requirement was "read 4,000 supplier invoices a month and populate a Dataverse table" — a scenario the prebuilt invoice-processing model resolves with no model training, no endpoint, and no inference infrastructure to operate.

Proposing Microsoft Power Platform AI features, including the AI hub, is an explicit design-domain skill on the AB-100 blueprint. It is the low-code end of the build/buy/extend spectrum, and on the exam it is the correct answer far more often than its reputation suggests.

1. Navigating to the AI Hub

The AI hub is not a separate product or portal. It is a navigation node inside the maker experience:

On the left pane, select ... More > AI hub .

Select AI models to browse and create models. (Makers can pin AI models to keep it permanently on the menu.)

From the same hub, makers reach prompts and agents , and the Monitor activity tile leads to AI Builder consumption reporting. In Power Automate, the AI Builder activity page is surfaced under Automation Center > Monitor > AI Builder activity .

The architectural significance of a single hub is consolidation: models, prompts, agents, and their consumption telemetry share one governance surface, one Dataverse environment boundary, and one security model, instead of being scattered across per-feature configuration pages.

2. The AI Builder Model Catalogue

AI Builder classifies every model by data type (documents, text, structured data, images) and build type (prebuilt or custom). Prebuilt models go straight to productivity; custom models are built, trained, and published by the maker for data unique to the business.

Data type Model Build type
Documents Business card reader Prebuilt
Documents Text recognition Prebuilt
Documents Receipt processing Prebuilt
Documents Invoice processing Prebuilt
Documents ID reader Prebuilt
Documents Contract processing Prebuilt
Documents Document processing Custom
Text Text generation (prompts) Prebuilt
Text Key phrase extraction Prebuilt
Text Language detection Prebuilt
Text Sentiment analysis Prebuilt
Text Text translation Prebuilt
Text Category classification Prebuilt and custom
Text Entity extraction Prebuilt and custom
Structured data Prediction Custom
Images Object detection Custom
Images Image description Prebuilt (preview)

Microsoft maps these to canonical business scenarios: receipt processing automates expense reports; contract processing extracts key clauses and data points; document processing automates customer application intake; prediction identifies fraudulent transactions; object detection automates inventory taking; category classification organises user feedback; sentiment analysis classifies customer feedback; key phrase extraction surfaces brand mentions.

The decision rule is data uniqueness. Use a prebuilt model when the document or text pattern is common across businesses — an invoice looks like an invoice everywhere. Use a custom model when the pattern is proprietary: your own claim form layout, your own product images, your own churn signal.

3. Prompts as a First-Class Power Platform Artefact

An AI prompt is a natural-language instruction backed by a large language model, created in the AI hub and consumed from Power Apps, Power Automate cloud flows, Copilot Studio agent flows, and Copilot Studio agents . Prompts accept typed inputs, return structured outputs, and are versioned as Dataverse artefacts inside a solution — which means they participate in normal application lifecycle management rather than living as untracked strings inside a flow action.

Two architectural consequences matter for AB-100:

Reuse across surfaces. A single governed "summarise this service case in three bullets for a field technician" prompt can be invoked from a canvas app, a nightly flow, and an agent. Duplicating that instruction into three places is the anti-pattern the prompt artefact exists to prevent.

Model choice inside a low-code surface. Foundry models for prompts are available inside Copilot Studio, Power Apps, and Power Automate, so a prompt is not permanently locked to a default model; model selection becomes a configuration decision subject to the organisation's permitted-model rule.

4. Monitoring, Consumption & Governance

The Monitor activity area of the AI hub provides tables for AI model and prompt activity, the data they process, and consumption tracking. The AI Builder activity page aggregates activity generated in Power Automate, Power Apps, and Microsoft Copilot Studio , defaulting to all models over the last seven days, with columns for:

Column Meaning
Processed time When processing occurred
Tool name The AI model or prompt name
Input Text input for text models, or Image / Document for others
Output The model response in the selected output format
Used in Consumption source: Power Automate, Power Apps, Copilot Studio, or Quick Test
Consumption Numeric consumption value for that run

Selecting an event reveals Processed by (typically the flow owner or the app user) and Model (the language model used for a prompt run).

Visibility is role-scoped. System Administrator and System Customizer see all activity for all AI models; Basic User and Environment Maker see only their own activity for models they can access. An architect designing a governance dashboard must account for this: a maker cannot self-serve tenant-wide consumption reporting.

4.1 The AI Event Table Is a Retention Obligation

Monitoring data is stored in the AI Event Dataverse table ( msdyn_aievent ), and Microsoft is explicit that it persists even if you delete the model, cloud flow, and app . Recording activity requires Create and Write access to that table. For text scenarios the table contains the input of the AI model predict actions , which means prompt inputs may constitute personal or sensitive data under the organisation's classification scheme.

The correct architectural response is a deliberate retention policy implemented as a recurring bulk-delete job in Dataverse:

Select Environments , choose the environment, then Settings .

Select Data management > Bulk deletion > New .

In Look for , select the AI Events table.

Add a filter, typically using OlderThanXDays , to match records beyond the retention window.

Name the job, choose an off-peak start time, and select Run this job after every n days to make it recurrent.

Bulk deletion requires the Bulk Delete privilege and is irreversible , so the filter must be tested before submission. The table can also be exported to CSV for audit evidence before purging.

4.2 Credits

Building and testing models and prompts does not consume AI Builder credits or Copilot credits. Running them in agents, agent flows, apps, or flows does . This asymmetry is the reason pilot programmes look free and production rollouts do not; the cost model must be sized on projected runs , not on the number of artefacts built.

5. Where Power Platform AI Fits in the Architecture

Requirement shape Correct Power Platform proposal

Extract structured fields from common business documents at volume AI Builder prebuilt model (invoice, receipt, ID, contract)

Extract fields from a proprietary form layout AI Builder custom document processing model
Score likelihood of an outcome from Dataverse history AI Builder custom prediction model

Reusable natural-language instruction shared by an app, a flow, and an agent AI prompt in the AI hub, referenced from all three

Classify or route free-text feedback Category classification or sentiment analysis
Conversational, multi-turn, tool-invoking business process Escalate out of AI Builder to Copilot Studio
Custom model training, fine-tuning, or frontier-model orchestration Escalate to Microsoft Foundry

AB-100 Exam Tip: When a scenario describes bounded, high-volume, non-conversational intelligence inside a business process — read the document, extract the fields, classify the text, predict the outcome — the intended answer is an AI Builder model surfaced through the AI hub, not a Foundry deployment and not a Copilot Studio agent. Copilot Studio earns its place when the interaction is conversational or agentic; Foundry earns its place when the model itself must be custom or frontier-class. Also watch for the naming trap: Microsoft Purview AI Hub is a compliance and AI-risk surface and is never the answer to a Power Platform maker-tooling question.

Loading diagram... The Power Platform AI Hub: Models, Prompts, Agents, Activity Monitoring and the AI Event Retention Chain