SharePoint: Create Copilot KB in 9 Min

Key insights

• Metadata is the root cause when Copilot returns vague or missing results.
  Without consistent, structured metadata, Copilot cannot reliably find or filter documents in SharePoint libraries.
• SharePoint Knowledge Agent reads custom metadata to return accurate, filtered results from document libraries.
  The agent lets you run queries without opening the library and relies on metadata like status and dates to be precise.
• Custom Copilot agent can be grounded to a specific library to handle complex, business‑specific queries.
  When grounded, the agent supports natural filters, grouping, and table-style outputs (for example, grouped by subcontractor).
• Natural language queries such as “get expired documents” or “show review dates this month” work reliably when metadata is correct.
  Practical tests show 100% accurate returns for queries that target custom columns like status, review date, and owner.
• Model key fields: use clear columns like document status, review dates, owners, and subcontractor and keep values consistent.
  Index frequently queried columns and keep naming conventions and data types uniform to improve performance and accuracy.
• Queryable knowledge base is the business payoff: with proper metadata, Copilot becomes a conversational interface to your documents.
  Actionable steps: define required columns, populate and clean data, test common queries, then iterate to improve results and compliance reporting.

Video Overview and Context

In a concise nine-minute video, Daniel Anderson [MVP] demonstrates how to turn SharePoint document libraries into a functional knowledge base for Copilot. He frames the problem simply: Copilot often struggles when documents lack meaningful metadata, and that gap undermines reliable search and reasoning. Consequently, Anderson walks viewers through Microsoft's new SharePoint Knowledge Agent and shows why structured metadata matters now more than ever. The video is practical and focused, with clear timestamps that guide viewers from the basics to an advanced custom agent build.

First, Anderson defines the core problem and then shows live examples that illustrate the solution in action. Next, he tests queries against a compliance library to confirm the Knowledge Agent's behavior with real metadata fields. Finally, he constructs a custom Copilot agent that queries by status, review dates, and subcontractor, demonstrating conversational access to filtered data. This step-by-step approach makes the video useful both for administrators and for content owners who manage document lifecycles.

Demonstration and Key Features

Anderson begins by introducing the SharePoint Knowledge Agent, which can read custom columns and return filtered results without users entering the library. For example, he runs queries like “get me all expired documents” and “show me documents with review dates this month,” and the agent returns precise, library-grounded answers. In addition, he demonstrates filtering by multiple metadata fields and grouping results, which highlights the platform's ability to handle compound, real-world queries. As a result, organizations can let people use natural language to find exactly the documents they need.

Moreover, the video shows that the agent does not rely solely on full-text content; it reasons over structured fields such as status, owner, and review dates. This capability reduces false positives and makes results more relevant for compliance and operational tasks. It also emphasizes that good metadata design is foundational: without it, the agent either misses content or returns vague answers. Therefore, the Knowledge Agent acts as a bridge between human intent and structured content, but only when that content is properly labeled.

Testing, Results, and Practical Takeaways

Anderson’s live tests produce striking results: the Knowledge Agent returns accurate, filtered outcomes when the metadata is correct. For instance, a compliance library test yielded 100% accurate matches for expired documents and items due for review this month. Consequently, this shows that the agent can support precise legal, audit, and operational queries that matter in regulated environments. Importantly, the video clarifies that these outcomes depend on consistent metadata entry and governance practices.

However, Anderson also notes practical caveats. If metadata columns are missing, inconsistently populated, or poorly named, the agent’s performance degrades quickly. Thus, organizations should invest time in planning metadata schemas, enforcing standards, and auditing data quality. In short, the Knowledge Agent can deliver reliable conversational search, but its value is tightly coupled to the quality of the underlying metadata and the discipline of content owners.

Building a Custom Copilot Agent

After demonstrating the Knowledge Agent, Anderson builds a custom Copilot agent that is grounded in one library. He configures the agent to handle complex prompts such as “list all document types with active status, grouped by subcontractor,” and then shows how results are organized into tables. This makes it clear that custom agents can extend site-level helpers into specialized tools for teams like contracts, compliance, or vendor management. Consequently, teams can access precise, tabular outputs without manual filtering.

In addition, Anderson highlights the user experience benefits of conversational access: team members can ask questions in plain language and get structured answers. Yet building a custom agent involves choices about scope, permissions, and governance, so administrators must balance ease of use with access controls. Therefore, when deploying custom agents, organizations should define ownership, logging, and review processes to ensure both utility and security.

Tradeoffs, Challenges, and Next Steps

While the video makes a strong case for metadata-driven intelligence, Anderson also acknowledges tradeoffs. On one hand, investing in metadata and custom agents yields faster, more accurate search outcomes that support compliance and productivity. On the other hand, that investment requires governance, user training, and ongoing maintenance to keep fields current and consistent. Consequently, leaders must weigh short-term setup costs against long-term operational gains.

Finally, Anderson points viewers toward deeper learning options for building Copilot-ready libraries, while cautioning that there is no one-size-fits-all template. Therefore, organizations should pilot the approach in a single library, measure accuracy improvements, and scale iteratively. In conclusion, the video provides a practical roadmap showing that, with disciplined metadata and thoughtful agent design, Copilot can become a conversational interface to a true knowledge base in SharePoint.

Keywords

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