Data Agent Architecture powered by Microsoft Dataverse 

Turning Hidden Data into Actionable Insights: Why Manual Processes Are Failing Your Business 

In every organization, data fuels critical business processes. Yet, much of this data is trapped in formats and workflows that make it difficult to access, understand, and use effectively. A common example is invoices arriving as PDF attachments via email—disconnected from the broader business data ecosystem. These files are hard to search, lack structured linkage to financial systems, and often require tedious manual intervention to process. 

For technical decision-makers, this isn’t just an operational headache—it’s a strategic liability. 

Manual data processing drains valuable resources, pulling skilled teams away from higher-value work. 

Data integrity suffers, increasing the risk of errors, missed payments, or compliance issues. 

Lack of discoverability means critical insights remain hidden, slowing down decision-making and reducing the agility of the business. 

In a data-driven world, relying on fragmented and manual workflows undermines the ability to scale, innovate, and stay competitive. The challenge is not having the data—it’s unlocking its potential efficiently and at scale. 

This post will explore how, with Microsoft Copilot Studio and Microsoft Dataverse, businesses can build intelligent agents that automatically turn fragmented data into structured, actionable insight. These AI-powered agents extract, validate, and enrich business data as it arrives, store it in Dataverse for reuse, and loop in humans only when needed. The result? Cleaner data, faster decisions, and a foundation for scalable, intelligent automation. 

Learn how Dataverse provides a secure, scalable, and agent-ready foundation for your data strategy in the Why Dataverse Overview. 

Figure 1: Built for Data Agents overview, with Dataverse as the underlining data platform for Agents. 

A Better Way: Activating Data with Autonomous Agents (with Dataverse at the Core) 

Modern businesses now have access to a more scalable, intelligent approach to data processing: Data Agent architectures powered by autonomous agents with human-in-the-loop oversight. 

What does this mean in practice? 

Autonomous agents handle repetitive data processing tasks intelligently: extracting, validating, and enriching data from unstructured sources such as emails, PDFs, tickets, and more. 

Dataverse serves as the unified data platform: offering a secure and consistent way to store business data—structured or unstructured—so it can be easily discovered, related, and used across business applications and workflows. 

Humans stay in the loop for oversight and decision-making: agents escalate exceptions, low-confidence matches, or contextual queries to human reviewers, who validate and finalize the data. 

The result: Intelligent, high-quality data delivered faster and more reliably, and centralized in Dataverse for seamless integration across business apps, analytics, and automation workflows. 

This architecture directly aligns with how Microsoft Copilot Studio agent flows work: orchestrated automation enables conversational agents (or flows) to autonomously process and route data, while intelligently involving human judgment when context or precision is crucial. With Dataverse at the heart, every interaction, enrichment, and validation feeds into a scalable, governed data platform—ensuring data isn’t just processed but activated for business value. 

While autonomous agents in Copilot Studio can operate independently using built-in tools, makers can enhance their capabilities further with tools like the Model Context Protocol (MCP). MCP isn’t required to build autonomous agents, but it opens up new possibilities for agents to process or store data in Dataverse. This bidirectional flow ensures AI solutions remain context-rich, action-oriented, and aligned with Microsoft’s broader commitment to openness and extensibility. 

Through integration with MCP, agents gain powerful data handling capabilities such as: 

Query: Discover table schemas and retrieve live Dataverse records via structured or natural-language queries; 

Knowledge/Search: Lets agents “chat” about data – they can search tables and answer questions contextually without brittle hand-coded logic; 

Upload (Create/Update): Insert new records or update existing ones in Dataverse, with built-in schema validation to maintain data integrity; 

Generate (Grounded AI): Run custom AI prompts grounded in real data (e.g. summarize a record or evaluate sentiment). 

Because the MCP server honors Dataverse’s data model and access controls, agents can safely reason over and act on enterprise data. In practice, developers can simply add a Model Context Protocol tool in Copilot Studio and point it to their Dataverse. The agent then automatically queries and updates that data as part of its workflow – for example, posting a generated summary back into a record. 

Figure 2: Shows adding the Model Context Protocol Server tool within Copilot Studio. 

Learn more with: 

Announcing new Microsoft Dataverse capabilities for multi-agent operations 

Get Started Building Intelligent Agents That Work for You 

As organizations move beyond basic automation, the next frontier isn’t just bots that complete tasks, it’s intelligent agents that understand, interact, and learn from business context. With Copilot Studio and Dataverse, you can create AI-powered agents that operate securely, respond to real-world triggers, and collaborate with humans to drive outcomes. 

We’ll walk through how to build and operationalize intelligent agents in six easy steps: 

Step 1: Trigger events from your business processes 

Agents can be triggered automatically based on business events—such as a new email, a service request, or a record added to a Dataverse table. These triggers, configured in Power Automate, initiate the agent’s workflow and response logic in Copilot Studio. 

Learn more with: 

Add an event trigger 

Step 2: Equip agents with intelligent tools 

Once triggered, the agent uses Copilot Studio capabilities to process requests: 

Knowledge Sources provide background information. 

Topics guide the structure of the conversation. 

Tools allow agents and MCP server(s) to perform tasks across systems. 

Conversation History maintains context for continuity. 

Instructions shape how the generative AI responds. 

Using these tools—along with Agent Flows and logic based on Model Context Protocol—agents can analyze data, perform multi-step actions, and interact meaningfully with users. 

Learn more with: 

Agent flows overview 

Use generative answers with knowledge sources 

Create and manage topics 

Add actions to custom agents 

Step 3: Use Dataverse as an intelligent data layer 

Dataverse brings structure and intelligence to your business data—so your agents can retain context, access relevant records, and contribute to a broader operational view. It gives Copilot agents secure, real-time access to the records, relationships, and history they need to work smarter across your apps and processes. 

With native support for the MCP, agents can draw from and contribute to Dataverse as part of a broader knowledge network. It’s a trusted foundation for building agents that are grounded in data, aligned with workflows, and ready to scale. 

Learn more with: 

Add a Dataverse knowledge source 

Use Dataverse table data as knowledge in Copilot agents 

Step 4: Enable autonomous agents with human oversight 

As agents become more capable, they also need to support human-in-the-loop scenarios. With conversation history and activity logs stored in Dataverse, business users can monitor agent performance, review outputs, and step in when escalation is needed—ensuring oversight, accountability, and trust. 

Learn more with: 

Build an autonomous agent in Copilot Studio. 

Unlocking autonomous agent capabilities with Copilot Studio. 

Manage Dataverse auditing 

Step 5: Coordinate workflows with the Agent Orchestrator 

Agent Orchestrator enables the user to coordinate more complex multi-step workflows by managing how and when tasks are executed across autonomous agents. For complex scenarios, it collects inputs from initial triggers, stores intermediate responses for reuse, and drives execution using Copilot Studio actions, Power Automate flows, or custom APIs. With built-in logic for queuing, task routing, and fallback handling, Agent Orchestrator ensures that each step is carried out by the most suitable agent—making it easier to scale intelligent, resilient automations across the organization. 

Learn more with: 

Orchestrate agent behavior with generative AI 

FAQ for generative orchestration 

Step 6: Govern secure access and responsible usage 

All agent interactions are secured through enterprise-grade governance controls: 

Role-Based Access Control (RBAC) ensures data access is scoped by role, table, and even row. Learn about RBAC. 

Least-privilege access, audit logging, and connector risk policies support safe and responsible agent behaviour. Least privilege and connector governance in Power Platform. 

Secure agent data access with an organized security framework that integrates platform technology, regulatory compliance, and administrative oversight—following best practices for managing access, reducing risk, and ensuring responsible agent behavior. Overview of role-based security in Dataverse. 

By combining Copilot Studio, Dataverse, and Power Platform tools, enterprises can create intelligent digital workers that adapt to the business, work alongside humans, and scale with confidence. This is how modern enterprises move beyond simple automation—and into a future powered by AI-first, data-smart systems. 

For more information watch Ryan Cunningham and Evan Lew discuss how to Build agent-first solutions with Power Platform and Copilot Studio. 

Figure 3: Workflow highlighting how Autonomous Agents interact with human and agent oversight, with Dataverse at the core. 

Real world application 

Velrada’s PowerRoster solution is helping frontline organizations reimagine workforce scheduling—reducing complexity and enabling smarter, faster decisions. At the heart of this transformation is ShiftLens, a new capability built with Copilot Studio that uses autonomous agents and intelligent workflows to automate manual scheduling tasks. ShiftLens is designed for high-pressure, high-variability environments such as healthcare and hospitality. When absenteeism is logged, ShiftLens records the key details into Microsoft Dataverse, updates the roster, and recommends staffing changes for manager approval. 

By eliminating the daily scramble to find last-minute replacements, team leaders and managers can focus more on delivery and less on logistics. This intelligent orchestration not only improves operational efficiency but also contributes to better staff satisfaction and customer outcomes. The demo below shows ShiftLens in action at a fictional care home—highlighting how AI-powered scheduling is making frontline workforce management more proactive, resilient, and human-centered. 

Figure 4: Video showcasing Velrada’s ShiftLens Intelligent Rescheduling product, powered by Microsoft Copilot Studio and Dataverse 

Conclusion 

The cost of manual processing isn’t just time—it’s missed insights, slower decisions, and operational drag. Copilot Studio and Dataverse offer a smarter path forward: intelligent agents that not only automate work but understand your data, collaborate with your teams, and evolve with your business. 

This is how leading organizations are moving from scattered workflows to coordinated, AI-powered systems. Whether you’re streamlining frontline operations or scaling data-driven decisions, now is the time to rethink what your business could do with agents that learn, act, and improve—built on a secure, governed foundation. 

Learn more about Copilot Studio and Dataverse

Explore the resources below to take the next step with data agent architecture powered by Copilot Studio and Dataverse. 

Learn more about Data Agent Architecture  

Explore how Microsoft is advancing intelligent agent design with Dataverse: Announcing new Microsoft Dataverse capabilities for multi-agent operations. 

Learn how to Connect to Dataverse with Model Context Protocol. 

Learn more about the Document Processor agent in Copilot Studio. 

From Microsoft Build 2025 

Learn how GenAI for Enterprise: Intelligent Apps and Agents with Dataverse & MCP enables intelligent, grounded agents built on your enterprise data and systems. 

Dataverse helps you turn business data into a platform for intelligent apps and automation with Dataverse for agents. 

Copilot Studio revolutionizes document processing and content generation with Agents in action: Document processing 2.0. 

Effectively leverage your enterprise Knowledge in Copilot Studio to enhance your agents with more relevant and contextual answers. 

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Source: Microsoft Power Platform