Dataverse Knowledge in Copilot Studio now searches the long notes and buried attachments that used to hide business-critical answers—thanks to new support for multi-line text columns and file columns, plus behind-the-scenes improvements that make repeated queries return more consistent answers.
Microsoft’s Copilot Studio has been evolving rapidly as a no-code / low-code environment for building AI agents that connect to enterprise data sources such as Dataverse. Until now, many knowledge-driven agents struggled when the facts they needed lived inside long text fields (customer feedback, legal clauses, freeform notes) or attached documents stored in Dataverse. The latest update addresses two of the most persistent friction points:
However, the operational reality of deploying AI agents into business processes requires deliberate governance. The twin promises of improved access and consistency must be paired with strict labeling, least-privilege access, and testing regimes to avoid accidental data exposure or over-reliance on answers that—while reproducible—may still be incomplete or contextually inappropriate for high-stakes decisions.
In short: treat Dataverse Knowledge’s new multiline and file column support as a powerful productivity lever—deploy it, but do so with the same rigorous policies and validation that govern any system touching sensitive enterprise data.
The platform now offers teams a more natural, repeatable way to ask questions of their buried data, from long notes to attached files—so the immediate challenge for IT and governance teams is to operationalize the capability safely: label, limit, validate, and monitor.
Source: Microsoft Dataverse Knowledge in Copilot Studio: Multiline Text Columns and File Columns. - Microsoft Power Platform Blog
Background / Overview
Microsoft’s Copilot Studio has been evolving rapidly as a no-code / low-code environment for building AI agents that connect to enterprise data sources such as Dataverse. Until now, many knowledge-driven agents struggled when the facts they needed lived inside long text fields (customer feedback, legal clauses, freeform notes) or attached documents stored in Dataverse. The latest update addresses two of the most persistent friction points:- enabling agents to search across multi-line text columns (so long-form notes and descriptions become first-class search targets), and
- allowing agents to index and retrieve from file columns stored in Dataverse, surfacing relevant passages from attachments rather than ignoring them.
What changed: feature breakdown
Multiline text columns become searchable
Previously many knowledge pipelines treated long text fields as a second-class citizen: indexed poorly, truncated for search, or ignored entirely. With this update, Copilot Studio’s Dataverse Knowledge indexing now includes full multi-line text columns, letting agents retrieve snippets and rank them by relevance in natural-language queries.- Why it matters: business artifacts such as support ticket transcripts, product reviews, contract notes, and internal commentary are often the richest source of tacit knowledge. Making multiline fields searchable transforms them from “stored but unusable” to active knowledge assets.
- Practical effects: ask a question in plain English (for example, “Which phones have the best reviews for creators?”) and the agent can return the exact review text even when that text lives in long review fields rather than a short summary.
File columns: attached documents now participate in search
Dataverse commonly stores attachments—PDFs, Word documents, text files—inside file columns. Prior constraints meant those files were often omitted from the knowledge index or only superficially scanned. The new capability lets agents search inside those files and return relevant excerpts.- Supported file content: searchable text in common document formats is indexed so answers may quote or summarize parts of attached documents. This is particularly useful for contract analysis, product spec lookups, and research notes.
- Current limitations: images and embedded tables within files are not yet searchable or returned as structured results, and the agent will only return results in the same language as the file’s content (i.e., English content must be queried in English). Multi-lingual, image, and table search inside files are listed as planned for a future update. Treat these as known constraints when planning knowledge ingestion.
Improved answer consistency and caching
One of the most user-visible changes is an internal improvement intended to make answers repeatable. In practice this means:- identical queries should produce the same grounded result when the underlying knowledge hasn’t changed, and
- results are more stable across repeated invocations (reducing nondeterministic answer variation that undermines trust for business users).
Why this matters for organizations
Unlocking buried value in existing data
Most enterprises already have troves of value locked in freeform notes and attachments. Making those fields first-class knowledge sources lets agents answer higher-value queries without manual data preparation or moving files into separate indexers.- Use cases that improve immediately:
- support agents that surface past troubleshooting notes from long ticket comments,
- legal teams finding contract clauses inside attached agreements,
- product teams extracting nuanced customer sentiments from long-form reviews.
Better operational reliability for repeatable tasks
Consistency in answers matters when decisions depend on them. If a compliance officer asks “find contracts expiring within the next 60 days,” they need the same results each time until the contracts themselves change. The consistency improvements are aimed at exactly this scenario.Governance and security are still central
Dataverse is the platform’s canonical store for business data and inherits the Power Platform’s governance, RBAC, and DLP policies. This integration is critical: AI-driven retrieval must respect label-based controls and sensitivity policies. Microsoft’s broader Copilot Studio ecosystem also has administrative controls for inventory, per-agent quotas, and policy enforcement—which are important when scaling agents across an enterprise.Technical and operational considerations
What the agent actually searches and returns
- Text extraction: file columns are parsed for textual content and multiline fields are indexed. Returned answers are grounded to the snippet or file location where the match occurred.
- Language matching: queries must use the same language as the file or field content. Multilingual file search is a planned capability but not included in this release. Treat multilingual knowledge bases accordingly.
- No image/table parsing (yet): embedded images and tables inside attachments won’t be interpreted. For any workflows that rely on table extraction (e.g., line-item invoice analysis) you’ll still need a separate document processing pipeline or wait for the announced future support.
Indexing behavior and freshness
- Index cadence: how frequently Dataverse Knowledge re-indexes file columns and multiline fields is a crucial operational parameter. If your knowledge content changes rapidly (e.g., daily contract edits), validate index refresh frequency in a test environment rather than assuming instantaneous freshness. If caching is applied to improve consistency, it can also create a temporal window where very recent edits aren’t immediately reflected—this is a tradeoff between reproducibility and recency.
Security, privacy, and compliance
- Data loss prevention (DLP): because Dataverse Knowledge operates within the Power Platform, DLP and MIP labeling apply—use these to prevent leakage of sensitive fields into agent outputs. Microsoft has introduced autolabeling and integration with Purview to help detect and classify sensitive Dataverse fields automatically. Implement label enforcement to ensure agents mask or block sensitive results in line with policy.
- Least-privilege access: restrict agent access to only the tables and columns required. Use the platform’s RBAC and sensitivity labeling to prevent inadvertent exposure when agents reference freeform notes that may include PII.
Cost and capacity controls
- Per-agent quotas: Copilot Studio provides per-agent message capacity and budgeting controls; implement these so that indexing, repeated queries, and heavy file searches don’t unexpectedly drive compute or consumption costs. Monitor tenant usage and billing metrics available in admin analytics.
Strengths — what this update does well
- Makes existing knowledge usable: instead of requiring content migration or re-structuring, this update lets organizations surface insights from the data they already collect in Dataverse.
- Natural-language first experience: business users can ask plain-English questions and get grounded answers, lowering the barrier to adoption for non-technical staff.
- Improved answer stability: consistency fixes reduce nondeterministic behavior that previously undermined user trust in agent responses—important for governance-heavy workflows.
- Platform governance integration: since Dataverse Knowledge sits inside the Power Platform stack, it benefits from existing governance, auditing, labeling, and admin tooling—making it easier to manage at scale.
Risks and limitations (what to watch for)
- Hallucinations and relevance errors: even grounded retrieval systems can present incorrect or mis-contextualized summaries. Validate outputs in regulated scenarios and maintain human-in-the-loop approval for critical decisions.
- Staleness due to caching and index cadence: caching that improves consistency can also delay visibility of recent changes. For time-sensitive queries (e.g., contract deadlines or inventory status), confirm refresh policies and consider hybrid designs where real-time data is pulled from a live query rather than relying solely on cached indexes.
- Language and format coverage gaps: images, embedded tables, and multilingual documents are not yet supported inside file columns. If your knowledge base relies heavily on scanned documents, images, or non-English content, the current release will be incomplete for those needs. Plan remediation or await the promised future update.
- Data governance misconfiguration: powerful search across long notes can surface PII, confidential negotiation notes, or other sensitive items that weren’t intended to be broadly discoverable. Enforce MIP labels and DLP policies before broad deployment.
- Operational complexity at scale: managing dozens or hundreds of agents requires strong admin tooling—inventory, quota management, data mapping, and quarantine capabilities are necessary. Use the admin center’s agent inventory and per-agent budgeting tools as part of a governance playbook.
Practical rollout guidance: a recommended checklist
- Prepare a non-production environment. Validate indexing and search behavior against a representative dataset first. Use the Power Platform admin center to manage agent inventory and capacity.
- Audit and label data. Apply Microsoft Information Protection (MIP) labels and Purview mapping to Dataverse tables and file columns before indexing. Enable autolabels where possible to avoid manual gaps.
- Clean and standardize multiline fields. Trim irrelevant metadata, normalize date and language formats, and annotate records where necessary to improve retrieval relevance.
- Control agent permissions. Limit each Copilot Studio agent to only the Dataverse tables, columns, and file groups it needs to reduce exposure risk. Use grouping features for logical separation of knowledge (for example, “HR policies” vs “Product specs”).
- Test queries and build evaluation cases. Use prompt evaluation/testing features to run suites of test questions and measure accuracy and coverage. Refine prompts and retrieval instructions based on failure patterns.
- Implement human-in-the-loop for high-risk outputs. For legal, finance, and compliance queries, gate agent outputs through reviewers before action.
- Monitor usage and costs. Configure per-agent budgets and monitor the tenant analytics dashboard to surface runaway usage or unexpected costs.
Example scenarios (how teams will use this)
Customer support knowledge assistant
- Ingest support ticket transcripts and engineer notes into Dataverse multiline fields.
- Build a Copilot Studio agent that searches both ticket text and attached diagnostic reports to surface past fixes and relevant knowledge articles.
- Benefit: faster mean-time-to-resolution and better first-contact resolution rates because agents can find solutions buried in long-form notes.
Contract lifecycle monitoring (legal/compliance)
- Store contract PDFs in Dataverse file columns and add contract metadata in table columns.
- Use natural-language queries like “find contracts expiring within the next 60 days” to pull a consistent list tied to document excerpts for review.
- Benefit: consistent and auditable outputs for renewal cycles and risk review. Be mindful of index freshness if contracts are updated frequently.
Product feedback and R&D
- Capture long-form product reviews and research notes in multiline columns.
- Query for comparative judgments (e.g., “Which phones are best for creators?”) and surface real review paragraphs that support any claim.
- Benefit: richer qualitative insight without manual summarization.
Verification notes and caveats
- The feature descriptions used in this article are based on the official Copilot Studio/Dataverse communications and related platform documentation supplied in the provided materials, which detail multiline and file column indexing, answer consistency improvements, and current limitations (no images/tables; language must match). Readers should treat the product limitations and future roadmap items (for example, multi-lingual and image/table search inside files) as vendor-forward statements that are subject to change and timeframe adjustments.
- Where platform behaviors (index cadence, caching windows, exact supported file formats) are critical to a production workflow, validate in a controlled tenant. Platform-level caching/consistency features can improve repeatability but may introduce latency between content changes and search results; teams should explicitly measure that window.
Final analysis — balancing opportunity and caution
This update materially raises the utility of Dataverse as an enterprise knowledge store by making multiline text and attached files searchable inside Copilot Studio agents. For organizations that already use Dataverse for operational records, support tickets, or contract storage, the change reduces friction and unlocks new conversational workflows that can speed decision-making.However, the operational reality of deploying AI agents into business processes requires deliberate governance. The twin promises of improved access and consistency must be paired with strict labeling, least-privilege access, and testing regimes to avoid accidental data exposure or over-reliance on answers that—while reproducible—may still be incomplete or contextually inappropriate for high-stakes decisions.
In short: treat Dataverse Knowledge’s new multiline and file column support as a powerful productivity lever—deploy it, but do so with the same rigorous policies and validation that govern any system touching sensitive enterprise data.
The platform now offers teams a more natural, repeatable way to ask questions of their buried data, from long notes to attached files—so the immediate challenge for IT and governance teams is to operationalize the capability safely: label, limit, validate, and monitor.
Source: Microsoft Dataverse Knowledge in Copilot Studio: Multiline Text Columns and File Columns. - Microsoft Power Platform Blog
