10.1 Configure and Monitor Computer Use

Microsoft Certified: AI Agent Builder Associate AB-620

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1 Introduction & Exam Overview

1.1 Current Exam Facts 1.2 Candidate Profile & Prerequisites 1.3 Skills Map & Study Strategy

2 Plan Integration, Identity & Channels

2.1 Plan Integration with Enterprise Systems 2.2 Plan Identity Strategy 2.3 Plan Channels and Deployment

3 Responsible AI, Security & Reuse

3.1 Plan Responsible AI Strategy 3.2 Security and Governance Considerations 3.3 Plan Reusable Agent Components 3.4 Design Agents for Internal or External Audiences

4 Agent Flows Fundamentals

4.1 Create an Agent Flow 4.2 Human-in-the-Loop Agent Flows 4.3 Configure Actions and Connectors in Flows

5 Agent Flow Monitoring & Resilience

5.1 Monitor Agent Flows 5.2 Input and Output Parameters 5.3 Error Handling in Agent Flows

6 Topics Configuration Core

6.1 Add Agent Flows to a Topic 6.2 Configure Agent Response Formatting 6.3 Add Tools to a Topic

7 Advanced Topic Responses

7.1 Custom Prompts for Advanced Responses 7.2 Custom Knowledge Sources for Advanced Responses 7.3 API and Send HTTP Request Responses

8 Generative Answers, Cards & Variables

8.1 Configure the Generative Answers Node 8.2 Configure Adaptive Cards 8.3 Manage Variables

9 Enterprise Knowledge Sources

9.1 Connect to Copilot Connectors 9.2 Connect to Power Platform Connectors 9.3 Connect to Azure AI Search

10 Add Tools to Agents

10.1 Configure and Monitor Computer Use 10.2 Configure MCP Tools 10.3 Tools via Existing Custom Connectors 10.4 Add REST APIs to an Agent

11 Multi-Agent Collaboration

11.1 Design Multi-Agent Solutions 11.2 Integrate a Foundry Agent 11.3 Integrate an Existing Copilot Studio Agent

12 Fabric Data Agents & A2A

12.1 Integrate a Fabric Data Agent 12.2 Multi-Agent Solutions with A2A Protocol

13 Integrate Agents with Azure

13.1 Generative Answers with Azure AI Search & Foundry 13.2 Foundry Model Catalog for Custom Prompts 13.3 Monitor Agents with Application Insights

14 Evaluate Agent Performance

14.1 Create a Test Set 14.2 Choose an Evaluation Method 14.3 Review Test Results

15 Application Lifecycle Management

15.1 Create a Solution 15.2 Add Existing Agents to a Solution 15.3 Create and Use Environment Variables 15.4 Power Platform Pipelines for Agents

10.1 Configure and Monitor Computer Use

Key Takeaways Explain This Topic AI

Computer use is a generative-orchestration tool that controls a Windows GUI with vision and reasoning so agents can automate apps and sites that lack APIs.

Required configuration fields are Name, Description, Model, and Instructions; optional settings include inputs, machine, credentials, human supervision, stored credentials, access control, and Enforce HTTPS.

Run targets include hosted browser (preview), Cloud PC pool (preview), and bring-your-own machines registered in Power Automate with computer use enabled.

Prefer dedicated machines, least-privilege accounts, allow lists, and stored credentials (internal storage or Azure Key Vault) over sharing broad maker access.

Monitor runs from agent Activity (Activity map and Transcript); advanced logging in Dataverse supports session replay, verbosity, retention, export, and optional Microsoft Purview audit.

Last updated: August 2026

Why computer use matters on AB-620

Configure and monitor computer use for an agent is a Domain 2 skill under Add tools to agents (about 40–45% of the exam). Microsoft expects you to know when computer use is the right tool, how to configure it safely, where it runs, and how to inspect what it did after a run—not only that the feature exists.

Computer use is a tool in Copilot Studio that lets an agent interact with websites and desktop applications by selecting buttons, choosing menus, and typing into fields. You describe the task in natural language; the agent runs that task on a configured Windows computer using a virtual mouse and keyboard. It is powered by Computer-Using Agents (CUA) models that combine vision with reasoning , so the agent can adapt when labels or layouts shift instead of relying on brittle pixel scripts alone.

Computer use requires generative orchestration turned on. Without it, the orchestrator cannot treat computer use as a selectable tool at runtime.

When to choose computer use vs API, connector, or MCP

Situation Prefer Why

Public or private REST API with OpenAPI and auth you control REST API tool or custom connector Structured inputs/outputs, DLP-aware connectors, lower ambiguity

Service already has a Power Platform connector Connector tool Managed auth patterns, reuse across Power Automate and agents

Tool catalog already published as MCP tools/resources MCP server tool Dynamic discovery of tools; standardized protocol

System is GUI-only (legacy client, vendor portal, no API) Computer use If a person can click through the app, computer use can too

Multi-step deterministic process with known connectors Agent flow Fixed path, capacity-metered actions, human-in-the-loop approvals

Exam trap: computer use is not a replacement for knowledge grounding. Use knowledge sources for Q&A over documents; use computer use for side-effecting UI work (submit forms, extract values from screens, navigate multi-page wizards).

Add computer use to an agent

Open the agent → Tools → Add tool .

Choose New tool → Computer use .

Select Add and configure .

Complete the four required fields , then Save .

Required field Role for the orchestrator and runtime
Name Display name so makers (and sometimes users) distinguish this tool from others

Description Short statement of what the tool does and when the agent should call it—critical for generative tool selection

Model CUA execution model (for example OpenAI Computer-Using Agent standard GA, Anthropic Claude Sonnet variants standard/experimental, Claude Opus premium experimental). Anthropic models need admin access to external models for the environment

Instructions Step-by-step task description including URLs and application names; this is the playbook the model follows

Optional configuration that appears on exams

Beyond the four required fields, review:

Inputs — Dynamic values that change each run (customer ID, invoice number). At execution, computer use combines instructions with input values.

Machine — Target machine or pool; refresh list, manage machines, or open machine details in Power Automate.

Connection — Connection identity used by the tool.

Credentials to use — Maker-provided (default; good for autonomous agents) or End user (each user must have machine access). Sharing an agent with maker-provided credentials means users can act with the original author’s access on that machine—treat as a high-risk design choice.

Human supervision — Email (Outlook) reviewers when the model detects potentially harmful instructions that could alter behavior. Response time limit ends the run if no one responds. Reviewers who did not initiate the run may not see the same activity context; assign authorized reviewers who understand the process.

Stored credentials — Website or desktop app sign-in secrets in Power Platform internal storage or Azure Key Vault (subscription, resource group, vault name, secret name). Map username + password/secret to site host or desktop process name (Task Manager Processes tab for process names).

Access control — Allow list of websites and desktop apps. Access control blocks interaction outside the list; it does not always prevent the model from opening another site (for example via a search bar). Design allow lists with that nuance in mind.

Enforce HTTPS — When on, computer use does not interact with http:// sites, reducing cleartext exposure risk.

Where computer use runs

Target Intent Notes

Hosted browser (preview) Quick web automation in a Microsoft-managed environment Not Entra-joined to your tenant; not for enterprise resource access or custom desktop apps

Cloud PC pool (preview) Scalable Windows 365 for Agents compute without BYO hardware Auto-scale, work/school account access, Entra joined and Intune enrolled for governance

Bring-your-own machine Customer-managed Windows endpoints Power Automate for desktop (supported version), register machine, turn on Enable for computer use

BYO requirements to memorize: install Power Automate for desktop with machine-runtime connectivity, register to the same environment , enable computer use on the machine settings page, and prefer a dedicated machine so interactive users are not interrupted. When a machine is busy, additional runs queue (Queued → Next to run → running).

Test, publish, and conversational vs autonomous use

After Name, Description, and Instructions are saved, use Test . The test experience shows a step log of reasoning/actions and a live preview of the machine. Use Stop testing to halt mid-run. Refine instructions if outcomes drift.

Publish the agent after configuration. Computer use works especially well for autonomous background agents. In conversational channels:

End-user credential mode requires each chat user to have valid machine credentials.

Runs may surface reasoning messages and screenshots in chat—plan privacy and channel suitability.

Monitor computer use

Open the agent Activity section and select a run.

Switch between Activity map and Transcript .

Transcript shows step-by-step reasoning and screenshots for each action.

Advanced activity (when logs are stored in Dataverse) opens a side panel from a computer use action with:

Session replay (screenshot series)
Per-action types, coordinates, user context, timestamps
Summary: instructions, inputs, duration, action counts, escalations, machine name/login
Websites and applications accessed
Credentials used
Export session logs for offline review

Admin configuration (Power Platform admin center → Environment → Settings → Products → Features → Computer Use):

Setting Purpose
Store logs in Dataverse Default on when Computer Use feature is enabled; off leaves only default activity logs
Verbosity All data (default), Data without screenshots, or Minimal
Log retention Default 7 days; custom up to long durations; 0 or -1 for keep forever per product guidance
Send audit logs to Microsoft Purview Independent; activity term CUAOperation

Capacity note: advanced logs use Database ( flowsession / binary metadata), Log ( flowlog ), and File storage—plan retention and verbosity with compliance and capacity together.

Transcripts can still include basic computer use logs and screenshots even when advanced Dataverse log settings differ—do not confuse agent transcript storage with the Computer Use advanced log toggles.

Scenario: Contoso legacy claims portal

Contoso’s carrier portal has no public API. Finance wants an agent to log in, open a claim, and copy status fields into Dataverse.

Prefer computer use over inventing screen-scrape HTTP tools that violate terms of service or break weekly.

Store portal password in Key Vault -backed stored credentials; enable access control for the portal host and browser process; turn on Enforce HTTPS .

Write instructions with exact URLs, field names, and failure recovery (“if MFA prompt appears, stop and escalate”).

Assign human supervision reviewers for risky instruction detection; set a realistic response timeout.

After pilot runs, inspect Transcript and session replay; tighten instructions where the model hesitated.

If the vendor later ships an OpenAPI, migrate to a REST or custom connector tool and retire GUI automation for that path.

Exam checklist

Generative orchestration must be on.

Name + Description drive when the tool is selected; Instructions drive how it acts.

Choose run target consciously (hosted browser vs Cloud PC pool vs BYO).

Treat maker-provided credentials and shared agents as a privilege boundary problem.

Use access control, HTTPS enforcement, dedicated machines, and stored secrets.

Prove behavior with Test, then Activity/Transcript and advanced Dataverse monitoring for production readiness.