Unlocking Intelligent Automation with Data Agent Architecture & Microsoft Dataverse

Manual copy-and-paste is the hidden tax most organisations still pay. Invoices arrive as PDFs, customer emails pile up, and frontline forms dribble in from every direction. Each document holds data you could analyse or automate - yet human effort is still required to make it usable. Microsoft’s data agent architecture offers a cleaner path: let Copilot Studio agents extract, validate, and enrich new data the moment it appears, then store the result in Microsoft Dataverse so every downstream app can trust it.

‍

What the architecture looks like in practice

Picture Dataverse as a governed library and Copilot Studio agents as diligent librarians.

Whenever a trigger fires - an Outlook rule, a SharePoint upload, or an API call - the agent:

1. reads the file or message,
2. applies a prompt (optionally grounded through the Model Context Protocol (MCP)),
3. writes a clean, relational record into Dataverse, and
4. flags any low-confidence rows for a human review queue.

That loop means data is structured the instant it enters your environment—and audit trails, relationships, and security policies are already in place.

‍

Organisations rarely work with just one document type. They juggle structured items such as forms, invoices, receipts, and identity papers alongside unstructured artefacts—memos, contracts, customer-feedback notes. Automating this entire journey—collection, classification, extraction, validation, transformation, and export—is a formidable challenge. A Document Processing Agent handles these steps out of the box. It arrives as a packaged solution, so you skip the time-consuming data-labelling phase that many traditional systems demand. And because the design keeps humans in the loop, you can push large volumes through the pipeline while still retaining oversight of edge-cases.

‍

Six Steps to Operationalize Intelligent Agents

1. Trigger events automatically – email, record creation, or API calls start the agent.
2. Equip with intelligent tools – knowledge sources, topics, MCP, and conversation history guide context. 
3. Use Dataverse as the intelligent data layer – agents read/write governed data in real time. 
4. Keep humans in the loop – low-confidence matches route to reviewers, all logged in Dataverse. 
5. Coordinate via Agent Orchestrator – queue tasks, manage retries, and route to the best agent for the job. 
6. Govern responsibly – RBAC, audit logs, and least-privilege policies secure every interaction. 

‍

Enter Data Agent Architecture

Microsoft Copilot Studio now lets you build autonomous agents—AI “digital workers” that ingest, validate, enrich, and store data in Microsoft Dataverse, escalating to humans only when confidence is low. The result is continuously cleaner data, faster decisions, and a governed single source of truth.

Start by modelling the Dataverse schema you want: tables, look-ups, option-sets, and any sensitive columns that will need field-level security. Then open Copilot Studio and create a new agent:

• Trigger it reliably. A shared mailbox or SharePoint library you already monitor is safer than a brand-new endpoint. 
• Wire up agent flows. Low-code flow steps handle extraction, validation, and enrichment out of the box.
• Ground with MCP when context matters. Point the agent at an MCP server if it needs domain knowledge or must push updates back.
• Keep humans in control. Configure an escalation path to Dataverse so reviewers can approve, correct, or reject entries. 

When multiple agents need to co-ordinate—say, one extracts invoices while another reconciles them—Copilot Studio’s orchestrator pattern keeps every step on beat.

‍