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M365 Developer E5 Sandbox provisioning failed despite active Visual Studio Enterprise subscription

AmithDebnath — Wed, 22 Jul 2026 06:03:15 GMT

Hi everyone,

I'm looking for some help with an issue I'm facing while setting up a Microsoft 365 Developer E5 sandbox.

I have an active Visual Studio Enterprise subscription (valid until 2029), and it is successfully linked to my Microsoft 365 Developer Program account.

What happened

I joined the Microsoft 365 Developer Program.
I selected Instant Sandbox.
I entered the required information and selected my Visual Studio Enterprise subscription.
The portal initially displayed the following message:
"It's taking longer than expected. Your setup is being processed. There is no action required from you at this time."
After waiting for nearly 24 hours, the dashboard changed to:
"You don't currently qualify for a Microsoft 365 Developer Program sandbox subscription."

What I've verified

Visual Studio Enterprise subscription is active and linked to my Developer Program account.
A developer tenant appears to have been created.
I can access the tenant through the Microsoft Entra Admin Center.
An Azure subscription named similar to devProgram_... is visible.
In the Microsoft 365 Admin Center (Billing → Your products), only Microsoft Entra ID Free is listed.
No Microsoft 365 E5 Developer subscription was provisioned.
No sample users or Microsoft 365 workloads (Exchange, Teams, SharePoint, etc.) appear to have been created.

My understanding

It appears that the developer tenant was created successfully, but the Microsoft 365 Developer E5 sandbox provisioning either failed or never completed. As a result, I don't have a usable Microsoft 365 developer environment for testing Declarative Agents and MCP integrations.

Has anyone experienced this before?

Is there a way to retry the E5 provisioning without creating a new Developer Program account?
Is there anything I can check on my side?
Does this require intervention from the Microsoft Developer Program team?

Any guidance would be greatly appreciated.

Thank you!

Changing the License Type and Key of a SharePoint Subscription Farm

kulldeepverma — Tue, 21 Jul 2026 19:12:10 GMT

Hi everyone,

I need some assistance with a licensing issue on a brand new SharePoint Subscription Edition farm. I have 6 servers total 4 Application/Search servers + 2 Web Front End/Distributed Cache servers and current state of farm is Fresh installation no custom code or solutions, no production content web apps configured yet.

The Problem: During setup, our systems team accidentally provided a SharePoint Standard Edition product key, which I used during farm creation. We actually require Enterprise Edition, but I am unable to convert the license type through Central Administration.

Issue 1: When I go to Central Administration > Upgrade and Migration > Convert farm license type, the Product Key text field and the OK button are both completely grayed out and disabled.
Issue 2: When I go to Central Administration > Upgrade and Migration > Enable Enterprise Features and enter our Enterprise key, the process fails with an Unknown Error / SkuUpgradeJob timer job exception ("Post setup configuration failed when attempting post setup configuration task -cmd services -install -cmd installfeatures").

What I Have Tried So Far:

Running PSConfig CLI: Ran psconfig.exe -cmd sku -upgrade -key from an elevated Command Prompt. It fails during the installfeatures phase.
PowerShell Feature Activation: Attempted to run Enable-SPFeature -Identity PremiumFarm -Force in the SharePoint Management Shell, but feature provisioning fails because the underlying SKU definition hasn't switched.
Service Account Privilege Adjustments: Tried restarting the SharePoint Timer Service (SPTimerV4) and changing its logon account to Local System / Local Admin to rule out file permission issues on the 16-Hive directory, but hit SQL permission/authentication blocks.
Clearing Timer Jobs and Restarts: Cleared stuck or failed SkuUpgradeJob instances under Timer Job Status, restarted the SharePoint Administration Service and Timer Service across all servers, and performed iisreset.

Since this is a completely fresh farm with no customization or data loss risk, what is the cleanest way to force the farm to accept the Enterprise product key without having to completely unjoin and rebuild the 6 servers?

Any suggestions or workarounds would be greatly appreciated!

Beyond Benchmarks: The Lifecycle of Measuring Agentic Quality in AI Content Management

sjauhar — Tue, 21 Jul 2026 15:56:41 GMT

By Sujay Kumar Jauhar, Spriha Chandrayan, Natasha Gaitonde, Zhen Lei, Reed Pankhurst, Anush Sankaran, Amrit Shandilya, Ryen W. White

Organizations put their most important content in OneDrive and SharePoint, trusting us to deliver AI experiences they can depend on. That trust runs across everything Copilot does with their content — surfacing the right information through retrieval, and, increasingly, driving agentic workflows that reason over documents and take action on their behalf. Living up to that trust is what we care about most, and it's why we put the quality of these experiences — and consequently the ways we measure that quality — at the center of how we build.

It turns out that this is a non-trivial problem, because quality in AI is rarely binary. An agent can call exactly the right tools and still produce an answer that's subtly, confidently wrong. A search result can be technically relevant and still miss what the user was really after. An AI-generated page can be well-structured and fluent and still omit the one detail that mattered. Quality here is continuous, context-dependent, and multidimensional — and when these experiences are serving hundreds of millions of enterprise users, measuring it demands scientific rigor across the full lifecycle of building AI products.

Across OneDrive and SharePoint — the knowledge platform for Copilot and Agents — we’ve spent the past year building a rigorous practice around evaluation: determining what to measure, developing the right methods to measure it, and learning how to act on those measurements to improve our systems and experiences.

In software engineering, tests verify correctness and telemetry monitors health. The boundaries are well-defined: a function returns the right value or it doesn’t; a service meets its service level objective or it doesn’t. AI evaluation requires an analogous discipline, but the problem is structurally harder — there is rarely a single correct answer, and quality depends on factors that shift with every user, task, and context.

This post describes the evaluation practice we are building across Copilot capabilities in OneDrive and SharePoint. It follows the arc of what we think of as the evaluation lifecycle — from rapid inner-loop validation, through rigorous offline measurement, to customer-grounded evaluation that evolves with real feedback, and ultimately to understanding whether our metrics predict what users actually experience. Each stage addresses a question the previous ones cannot, but they don’t run in a straight line. What we learn downstream can reshape the data and methodology of upstream measurements. That’s what makes this a lifecycle — and together they form a strategy for building AI systems that aim to earn user trust at enterprise scale.

The stages of the evaluation lifecycle

We're on a journey — learning and evolving as we build this practice. Some of the things we're actively working on, omitted from this blog post for the sake of brevity, are: holding ourselves to public, industry-standard benchmarks such as GAIA and GDPval, with more in the pipeline; and investing in the foundations that keep evaluation trustworthy, by generating realistic synthetic evaluation data so our test sets keep pace with new models, and monitoring the health of our benchmarks for saturation.

The inner loop: "Did I break anything?"

The first requirement of any AI development workflow is a fast feedback signal. Teams are experimenting constantly on internal builds — adjusting prompts, swapping model versions, rearchitecting retrieval pipelines — and they need to know within minutes whether a change helped or hurt.

That is the role of our unit-level evaluations: a rapid inner loop for feature crews. A developer modifies a system prompt or adjusts the grounding context, runs it against a targeted set of test cases, and gets immediate feedback on whether key behaviors are preserved. These checks are fast, focused, and designed to catch regressions before they compound.

The distinction from traditional unit testing is worth noting. In software, a test asserts an exact expected output. In AI, you are validating behavioral properties — coherence, completeness, tool selection, factual consistency — against approximate expectations. The pass/fail boundary is softer, but the engineering purpose is the same: give teams confidence to iterate without fear of silent degradation.

One of the crews building Copilot's agentic features over SharePoint Lists knows this loop well. They keep a large set of prompts that engineers run whenever they change something — a tweak to a tool's description, a different setting passed to the model, a reworded prompt.

Many of those questions run over the same synthetic data: lists of bakery orders, each row carrying details like the baker, the delivery agent, the profit on the order, and its status. The same lists come in a range of sizes, from a few thousand rows up to a million. The largest are used to push the system's limits but the inner loop lives at the lower end. There, most questions are ones the system has gotten right for a long time, so when a change quietly breaks one of them, it shows up right away. An engineer can try an idea, run the set quickly, and whether they've introduced a regression.

An example of what the schema of our synthetic dataset looks like

Fast iteration checks are essential, but they only tell you whether something regressed. They cannot tell you how good the system actually is.

Offline evaluation: "How good is this, really?"

This is the domain of our offline evaluation practice. We maintain curated, scenario-focused benchmarks evaluated in a controlled harness that reflect the tasks and content types our enterprise users encounter, alongside public benchmarks that situate our systems in the broader AI landscape. The goal is to bring each AI scenario to a state of decision-readiness: explicit success criteria, representative evaluation datasets, metrics we attempt to validate against real outcomes, and systematic coverage of known failure modes.

We try to do this rigorously by running evals against the real product. Each test case is a plan — a short script of things a user might ask for — paired with criteria (or assertions) describing what a good outcome looks like, sorted by how much each one matters: Critical, Expected, or Aspirational. An LLM plays the user: it drives the live product through the browser, sends the opening request, and then reacts the way a person would, confirming an action card here or answering a clarifying question there, so the system is exercised end to end.

When the conversation finishes, the harness gathers the evidence of what actually happened — the responses in the chat, the state of the page, and screenshots where the judgment is visual. That evidence goes to a second model acting as judge — the increasingly standard "LLM-as-a-judge" approach — which scores the run against the original assertions. And because a model grading a model is only as trustworthy as its agreement with people, those scores are calibrated against human raters.

Using a high-priority, curated slice of these evals, the team ran big proposed changes side by side to see which ones came out ahead. One of those comparisons looked at how the orchestrator is told about the tools it can use. At one extreme, you hand the model the full description of every tool up front; at the other, you give it only short, tweet-length summaries and let it pull a tool's full description and parameters only when it decides it needs them.

Configuration	Quality %	Latency vs. default	Cost vs. default
Baseline	83.0	1.00x	1.00x
Default tool discovery	90.3	1.11x	0.62x
Rearchitected tool discovery	85.6	0.97x	0.73x
Semantic tool search	92.8	0.95x	0.74x
Hybrid	93.5	0.82x	0.62x

Table 1: A comparison of the different approaches for tool selection we experimented with. Based on internal evaluation results using our internal test environment, and test configurations of the product. Quality was measured through LLM judgement of AI responses based on a set of criteria, latency was measured by the harness running the evaluations, and cost is based on publicly disclosed price heuristics for token usage for the LLM API being used for orchestration. Results were calculated from 500 scenario tests based on synthetic data.

The table above summarizes our results from an internal offline evaluation on a curated slice. In this evaluation, we found that the baseline which loads everything up front was, predictably, the most expensive. The out-of-the-box tool-discovery method cut token usage sharply — but was a bit slower. Pushing further, a rearchitecting of the orchestrator for tool discovery did speed things up, but at the cost of quality. Meanwhile semantic tool search improved on both quality and latency relative to the baseline but wasn’t as cost-efficient as the default tool discovery method. Overall, in this evaluation, a strong balance of quality, token usage, and latency came from a hybrid — the standard tool-learning architecture with a few targeted semantic-search changes, which still resulted in a more than 35% reduction in token usage when compared with full tool descriptions.

Offline benchmarks, however carefully constructed, have an inherent limitation. They represent a fixed snapshot — based often on what product teams believe will be important use cases, particularly for features that have not yet shipped to real customers. As the product evolves and real usage patterns emerge, the gap between what our benchmarks measure and what actually matters can widen.

The evals flywheel: "Are we measuring what users care about?"

This is the motivation behind what we call the Evals Flywheel: a closed-loop system that translates real user dissatisfaction directly into better evaluations and, ultimately, better systems. In software engineering, there is a well-established discipline of turning production bugs into tests. In the AI analog, user feedback provided with appropriate consent helps produce new evaluation data, systematic failure diagnoses, and targeted improvements.

Take for instance a user that consents to share feedback with us (via a thumbs down). At first glance, this seems like a single interaction. But to us, it's an opportunity to learn. We investigate what happened, identify the underlying reason for the user dissatisfaction, and ask a critical question: was this an isolated mistake, or does it reveal a broader weakness in how the system understands documents like this one?

Here's a representative example. A user uploaded a 120-page quarterly financial report packed with dense tables, nested headers, footnotes, and appendices. They asked a straightforward question: "What was our actual spending in Q3?" The answer seemed like it should be easy — the relevant numbers were all in the document. The assistant responded confidently with a figure from one of the summary tables; but it cited the budgeted amount, not the actual spend.

The mistake stemmed from the document's complexity. The table spanned multiple pages, with a top-level "Q3" header extending across several sub-columns such as Budget, Actual, Forecast, and Variance. On its own, that's one mistake in one document. However, it pointed at something more general: whenever a table uses a heading that spans multiple columns, or stacks two rows of headers, or tucks a small table inside a larger one, then the link between a header label and its value turns fragile. Understanding the text is often not enough — the assistant must also accurately preserve and reason over intricate document structure, table relationships, and context that can span dozens of pages.

An evaluation document built in an automated manner that deliberately reproduces the exact structure of the table that was the root cause for the customer dissatisfaction.

Hence, instead of patching that single file, we built a family of evaluation documents that deliberately stress that exact structure. Each eval document pairs a question with the single correct answer and a short list of "tempting but wrong" answers which are the nearby values a confused reader would most likely grab. That last part is the key: an eval that only checks for the right answer can be passed by luck, but an eval that includes likely distractors tells you whether the system is genuinely reasoning or just guessing.

This example reflects a repeatable method. The first step is resisting the urge to treat a user dissatisfaction as an isolated bug. Instead, we ask why it happened and not "what's the right answer for this question." That root-cause identification is what turns the user feedback into a useful signal. The payoff is a flywheel between real-world experience and offline evaluation. The improvement ships back into the product, where future interactions validate it — and surface the next gap. Each loop widens our coverage and, just as importantly, helps us prioritize the improvements that matter most to the people using the product.

But it raises a deeper question: when our benchmarks are representative of user concerns, do improvements actually translate into better experiences in production?

Real-world insights: "Does this actually matter to users?"

It is possible to improve a benchmark score and have it mean nothing in practice. A model that scores five points higher on an offline evaluation may produce no detectable difference in user satisfaction or task success. Conversely, a change that barely moves the needle offline might transform the user experience in unexpected ways.

This gap between offline measurement and real-world impact is the focus of our online evaluation work. Through production telemetry, we study user-AI interaction patterns at scale. The objective is not simply to monitor system health, but to develop an empirical understanding of how users experience our AI features in context.

In traditional software, production telemetry provides crisp, well-defined signals: uptime, latency percentiles, error rates. In AI, the equivalent signals — satisfaction, intent alignment, task success — are inherently noisier and harder to define. The central challenge here is alignment: do our offline metrics predict real user value? Answering that depends first on being able to measure value reliably in production at all.

Consider a recent experiment on one of our top-of-funnel entry points — the most common way users first reach the assistant. The team suspected that engagement was low (around 1%) simply because users weren't noticing the entry point. So they ran a user-level A/B test across a sample of 900,000 Copilot-licensed users over 21 days, swapping the entry point's icon for a more contextually relevant one. The effect was a statistically significant 42% lift in click-through, alongside a 1.7% increase in active users.

That result is encouraging, but it also illustrates a subtle nuance. We moved an engagement metric sharply without touching quality at all — this tells us that we had an education gap, but didn’t inform our understanding of the AI system’s performance.

To get closer to that distinction, we looked at kept rate: the share of AI responses where the user does something that signals the answer was worth keeping — copying the result, clicking through to a citation or a suggested follow-up, or providing a thumbs up. And because it's captured passively, from actions users take anyway, it scales to every response rather than the small fraction where someone leaves explicit feedback.

Even an indirect, partial signal like kept rate lets us close the offline-online loop. Once we have a read on what users valued, we can start to ask how well it correlates with our offline benchmarks. When offline gains track with a rise in kept rate, for example, it's early evidence the benchmark is steering us toward something real; when they are uncorrelated, it's a warning that we may be hill-climbing in a wrong direction.

Beyond quality

Quality matters — but in production systems, it is entangled with cost and latency in ways that cannot be evaluated in isolation. A high-quality response that is unacceptably slow degrades the user experience regardless of its correctness. Or a frontier model that is marginally better than a smaller alternative may not justify the cost differential.

This is the next frontier of our evaluation work: learning to reason about quality, cost, and latency as a tradeoff surface rather than independent dimensions. The goal is to build the instrumentation and frameworks that make these tradeoffs visible and deliberate, so that teams can make principled decisions about how they ship.

To make this concrete, consider some early findings from our internal evaluations. We took a range of system configurations — models, reasoning levels, tool access — and, for a set of representative tasks — QA over lists, the AppWorld public benchmark, and a collection of complex multi-step plans that stress-test the Copilot in SharePoint agent — plotted each one by the quality it achieved against what it cost to run. The result is a Pareto frontier: the curve of the best quality reachable at each price, with everything below it dominated by something strictly better.

The Pareto frontier of cost vs. quality estimated across three different benchmark datasets from an internal evaluation run.

Two things stand out. First, different tasks live in different performance regions. Simple, factual lookups like those in ListsQA cost fractions of a cent; harder, more agentic tasks like those in AppWorld and Copilot in SharePoint run to cents or dollars; there is no single answer to what "good" costs. Second, the shape of each curve tells you where spending more is actually worth it. Where the frontier rises sharply, additional cost buys real quality; where it flattens, you have hit diminishing returns, paying more for gains a user may not notice.

In short, the decision stops being "is the quality good enough to ship?" and becomes "which point on this curve is right for this scenario?" given budget and latency constraints — a question a feature crew can answer, with the tradeoffs in front of them.

What we've learned so far

We are still early in this work. Models are improving rapidly, user expectations are evolving, and the definition of quality continues to shift. But several principles have become clear to us.

No single evaluation approach is sufficient. Inner-loop checks catch regressions but cannot establish absolute quality. Offline benchmarks provide rigor but go stale. Customer feedback grounds evaluation in real needs but covers only the cases where dissatisfaction is expressed. Online metrics reveal real behavioral patterns but cannot always explain the mechanisms behind them. A credible evaluation practice requires all of these, operating in concert and informing each other.

Evaluation is not a gate you pass before shipping — it is a continuous discipline that evolves with the product. The teams that build the best AI experiences are not necessarily those with the highest benchmark scores, but those with the clearest understanding of where their systems fall short and the most systematic approach to closing those gaps.

And measuring what matters is itself a scientific problem, one that demands the same rigor we apply to the systems being measured. Developing evaluation methods that are reliable, predictive of real user value, and robust to the rapid evolution of AI capabilities is core to our mission across OneDrive and SharePoint. It is how we work to earn the trust of the enterprise users who depend on these experiences, and we are committed to advancing this work and sharing what we learn.

SharePoint List Web Parts - New UX

bryanfrumkin220 — Mon, 20 Jul 2026 22:11:33 GMT

Hey everyone,

I am hoping I can get some assistance on the following request.

My organization's using SharePoint to manage multiple processes, but am confused with a new UX update that's come in the last few days.

Please look at the following example, which is a list with a modern calendar view:

For all of my lists, that are used as a web part on a different Site Page, have been given an ability to add a 'New Item', by selecting the title name, which's very frustrating as all of our list processes already have their own system to collect information and it seems as though I cannot turn off this functionality.

Any help would be very much appreciated.

Rest API Error:"The parameter Members does not exist in method AddTenantTheme"

AmarDhan — Mon, 20 Jul 2026 20:25:15 GMT

Using the following code.

$colorPairs = @{

"themePrimary" = "#003538";

"themeLighterAlt" = "#cbe6e7";

"themeLighter" = "#a1cfd1";

"themeLight" = "#7cb8bb";

"themeTertiary" = "#5ba2a5";

"themeSecondary" = "#3f8c90";

"themeDarkAlt" = "#28767a";

"themeDark" = "#166064";

"themeDarker" = "#094b4e";

"neutralLighterAlt" = "#f8f8f8";

"neutralLighter" = "#f4f4f4";

"neutralLight" = "#eaeaea";

"neutralQuaternaryAlt" = "#dadada";

"neutralQuaternary" = "#d0d0d0";

"neutralTertiaryAlt" = "#c8c8c8";

"neutralTertiary" = "#98a9da";

"neutralSecondary" = "#4863b4";

"neutralPrimaryAlt" = "#113191";

"neutralPrimary" = "#002081";

"neutralDark" = "#001963";

"black" = "#001249";

"white" = "#ffffff";

}

$content = @{

"name" = "MyCustomTheme"

"themeJson" = $jsonBody

} | ConvertTo-Json

$apiUrl = "https://<<domain>>-admin.sharepoint.com/_api/thememanager/AddTenantTheme"

Invoke-PnPSPRestMethod -Url $apiUrl -Method "Post" -Content $content

Tried many different combinations but kept getting

Invoke-PnPSPRestMethod: {"odata.error":{"code":"-1, Microsoft.SharePoint.Client.InvalidClientQueryException","message":{"lang":"en-US","value":"The parameter Members does not exist in method AddTenantTheme."}}}

Wondering if somebody can run this code successfully and let me know what I am missing.

I have trouble with phone number and Microsoft Authentication when login. Where can I find support?

winofwin292 — Sun, 19 Jul 2026 03:29:06 GMT

Hello,

Currently, I am unable to log in to my email address removed for privacy reasons account. I have the password. I have a backup email address, but when I try to log in, at the authentication step, I don't have the option to receive the OTP code via email.

I can only choose to have it sent to my phone number or the Microsoft Authentication app.

But when I tried sending it to the phone number, I got the following error:

Error Code: 399287

Request Id: 4d13566b-7aa4-4b3f-a03b-0c9416841000

Correlation Id: 019f785d-514f-7eaa-b1a3-ee66302634ae

Timestamp: 2026-07-19T03:13:40Z

Also, I can't access the Microsoft Authentication app because I've changed devices multiple times.

I couldn't find the support form, so I replied to this email.

With the admin account password and backup email, how can I log into the admin panel?

Sincerely,

Thang

External Users Unable to Access Microsoft List Form

virendrak — Fri, 17 Jul 2026 21:00:58 GMT

We have created a Microsoft List and are using the default Microsoft Lists Form for data entry / intake form.

Internal users can access the form URL and submit items successfully. However, external users are unable to access the form link, even though they have been granted Contribute permissions to the SharePoint list.

External users can:

Access the list
Create new items using the + New button
Edit items based on their permissions

However, they cannot access the dedicated Microsoft Lists Form URL and getting following error.
This link is only available to internal users. This link is not available to you.

Our requirement is to allow external users to:

Open the Microsoft Lists Form URL
View the form
Submit new items

without providing additional access beyond what is required for form submission, and without using Microsoft Forms.

Is guest/external user access to the Microsoft Lists Form experience supported? If so, what configuration or permissions are required to enable external users to access and submit items through the form URL?

Power Automate List Formatting - Compact List Rows

bryanfrumkin220 — Fri, 17 Jul 2026 20:00:08 GMT

Hey everyone,

I am hoping I can get some assistance on the following request.

I am currently using a SharePoint for leadership to request creation of an item, once the request goes in multiple other things happen to this sharepoint list.

One of those things is to create a list view and apply formatting, but I cannot figure out how to control my row height via View Formatting or an HTTP request to SharePoint.

Would greatly appreciate any assistance!

Storing and organizing performance review data - SharePoint lists or something else?

HarryP — Fri, 17 Jul 2026 14:49:08 GMT

My team has been asked to move our performance review records from a legacy HR system into something within M365. Someone suggested SharePoint lists but I'm worried about permissions (employees shouldnt see each others reviews), workflow complexity, and whether it'll scale. We have about 400 employees. Is SharePoint the right tool for this or should I be looking at something purpose-built that integrates with our M365 environment?

SharePoint SE - Distributed cache service is not enabled in this deployment.

MeFromMars — Sun, 19 Jul 2026 09:57:05 GMT

Hi everyone,

I have been struggling for a few weeks now with a Distributed Cache issue in my SharePoint farm. The service configuration seems completely out of sync between my two Web Front Ends (WFEs).

1 Root Cause / History:

Originally, the Distributed Cache was working perfectly. However, after a Windows Update, both of our original WFE servers broke completely (we couldn't repair them using DISM). Because of this, we built two brand new WFE servers to replace them.

Crucially, because the old servers were broken, we performed the steps in this order:

1. We built and added the two new WFE servers to the farm first.

2. After adding the new ones, we removed the two broken ones using the Configuration Wizard, and subsequently deleted them from Central Administration since they were still showing up.

Because the old servers were never gracefully decoupled from the Distributed Cache before they died and were removed, the internal Distributed Cache configuration in the SharePoint configuration database is now corrupted.

2 Environment & Farm Topology:

• SharePoint Version: SharePoint Subscription Edition (SE)

• OS: Windows Server 2025

• Database: SQL Server 2025

• Topology: 2 Web Application servers (App servers) and 2 brand new Web Front End servers (Server 1 and Server 2).

• Note: All required Distributed Cache firewall ports (22233, 22234, 22235, 22236) are completely open on both servers.

3 The Current Symptoms on the New WFEs:

• Server 1: The Windows Configuration Wizard runs and completes successfully. However, in Central Administration, the Distributed Cache service status for this server is stuck on "Error Stopping".

• Server 2: Distributed Cache does not work at all. Running Get-SPCacheHost prompts for a HostName, indicating it cannot find the host in the cache configuration.

• Cache Health Error: Running Get-SPCacheClusterHealth fails completely with:

ErrorCode<ERRCAdmin040>:SubStatus<ES0001>:Failed to connect to hosts in the cluster

• Health Analyzer Errors:

1. "Distributed cache service is not enabled in this deployment."

2. "Server role configuration isn't correct." (Specifically flagging Server 2).

4 What I Have Tried So Far:

1. Cleaned up old servers from Config: I exported the cache cluster config (Export-SPCacheClusterConfig), manually removed the two old, decommissioned servers from the XML file, and imported it back.

2. Repair via Central Admin: Tried to use the built-in repair and fix options in CA, but without success.

3. Attempted Fix from Microsoft Learn: I followed the steps outlined in this Microsoft Learn thread regarding a renamed server issue: [https://learn.microsoft.com/en-ie/answers/questions/5907941/distributed-cache-not-working-on-a-renamed-sharepo]

4. Forced Deletion on Server 2: Because Remove-SPDistributedCacheServiceInstance failed with a cacheHostInfo is null error, I used PowerShell to force-delete the service instance object on Server 2:

PowerShell

$instanceName = "SPDistributedCacheService Name=SPCache" $serviceInstance = Get-SPServiceInstance | ? { ($_.Service.ToString()) -eq $instanceName -and ($_.Server.Name) -eq "Server02" } $serviceInstance.Delete()

5. Re-adding Server 2: When I now try to re-add the instance on Server 2 using Add-SPDistributedCacheServiceInstance, it fails with:

ErrorCode<HostAlreadyPresent>:SubStatus<ES0001>:Host name Server02 is already present in cluster configuration.

6. Configuration Wizard on Server 2: Running the SharePoint Configuration Wizard (PSConfig) on Server 2 fails with:

An exception of type System.InvalidOperationException was thrown. Additional exception information: cacheHostInfo is null.

7. Farm Rejoin: I completely removed Server 2 from the SharePoint farm and added it back again. Unfortunately, this did not resolve the Distributed Cache mismatch.

5 Summary of the Problem:

Server 1 is stuck on "Error Stopping" in CA. Server 2 is completely disconnected from the cache cluster (Failed to connect to hosts in the cluster). SharePoint throws HostAlreadyPresent when trying to add Server 2, but throws cacheHostInfo is null when trying to configure or remove it, while complaining that Server 2's role configuration is incorrect.

Any help would be greatly appreciated!

What’s New in Copilot in SharePoint: July 2026

julieseto — Thu, 16 Jul 2026 16:05:23 GMT

Copilot in SharePoint helps you do more with your content — ask questions, run workflows, and create sites, pages, interactive reports, and Office files using natural language. June was a big month: starting June 16 Copilot in SharePoint began rolling out as on by default for everyone with a Microsoft 365 Copilot license, and we shipped a wave of new capabilities for creating content, automating work, and getting trustworthy answers. Here’s everything that’s new.

🛠️ Create content with natural language

Create Word, Excel, and PowerPoint files

What’s new: Ask Copilot to create a Word document, Excel workbook, or PowerPoint deck directly from your content in SharePoint — no blank-page start required.

Why it matters: Turn the knowledge already living in your sites into finished, shareable deliverables in minutes, without copy-pasting across apps.

Sample use case: A program manager turns a project site’s notes into a first-draft status deck before a leadership review.

Try this prompt “Create a press release as a Word doc based on the sales performance dashboard [doc link]. Summarize key results, highlight top growth areas, call out trends, and frame it for an internal leadership audience.”

If the player doesn’t load, open the video in a new window: Open video

Generate interactive HTML reports

What’s new: Copilot can build an interactive HTML report from content stored in SharePoint.

Why it matters: Get a polished, shareable, interactive view of your information — great for readouts and dashboards — without manual formatting.

Sample use case: A team lead generates an interactive summary of a document library to share progress with stakeholders.

Try this prompt “Create an interactive dashboard based on this quarter’s sales performance data [folder link]. Include total revenue, quota attainment, top and bottom performing regions, and trend over time. Add filters for region and product, and make it suitable for a leadership review.”

If the player doesn’t load, open the video in a new window: Open video

📁 Work with files through chat

Organize files and folders

What’s new: Manage your library right from chat — Copilot can help you organize files and folders conversationally.

Why it matters: Tidy up and restructure content without clicking through menus — just describe what you want.

Sample use case: Cleaning up a messy project library at the end of a sprint.

Try this prompt “Based on the contents of this folder, please suggest a folder structure to better organize my files. Show me a mapping structure, and once I approve, apply it.”

If the player doesn’t load, open the video in a new window: Open video

Share a file

What’s new: Ask Copilot to share a specific file with a specific person — directly in the flow of chat.

Why it matters: Skip the hunt-and-click sharing dialog; share what you’re looking at, right when you need to.

⚙️ Automate your team’s work

Approvals

What’s new: Natural language configuration for simple request tracking and approvals on your site.

Why it matters: It's now easy to use built-in approval tracking for managing your sign-offs, requests, and reviews all within SharePoint list or library, no custom approval flows required.

Sample use case: A content manager sets up a quick approval step for documents before they’re published.

Try this prompt “Help me track approval requests for this library and create a view grouped by status. Highlight overdue or stalled items and suggest next steps.”

If the player doesn’t load, open the video in a new window: Open video

Rules

What’s new: Configure simple email notifications when content or metadata changes.

Why it matters: Stay on top of what matters — get notified automatically instead of manually checking for updates.

Try this prompt “Send an email reminder to the file owner, 5 days before the review by date.”

Quick Steps

What’s new: Add your favorite actions right into the grid for one-click reuse.

Why it matters: Put the actions you repeat most within instant reach, reducing routine clicks.

Try this prompt “Add a button to update the review by date to today and set the status as under review.”

🧩 Build and use Skills

Built-in (out-of-box) Skills for the full Skill lifecycle

What’s new: Copilot now uses built-in skills to help you create your own skills, set up automation, and initialize a site (including a SharePoint.md starter).

Why it matters: It's now easier to turn your team’s repeatable processes into reusable AI skills — without starting from scratch — so work stays consistent and scalable.

Try this prompt “Follow approved layouts and branding — then save this as a skill so my team can reuse it.”

If the player doesn’t load, open the video in a new window: Open video

Discover skills with /Skills

What’s new: Anyone on a site can type /Skills to see the list of skills available in that context.

Why it matters: /Skills makes your team’s custom skills more discoverable, so people actually use the shortcuts you’ve built.

💬 A clearer, more trustworthy chat experience

This month brings a set of refinements that make answers easier to read, verify, and trust:

Better citations — Citations are now compact and clickable, so you can jump straight to the source.

Sensitivity labels in chat — Copilot now surfaces sensitivity labels on content to help prevent oversharing and meet privacy requirements.

Clickable links — All links in chat are now properly formatted and clickable.

Reasoning transparency — For reasoning models, Copilot can now show its chain-of-thought so you can follow how it reached an answer.

Try this prompt “Based on the documents in this library, what are the key deliverables for the Q3 product launch and who is responsible for each?”

⚡ Faster ways in — the Copilot button

1-click Q&A — A prompt box right in the Copilot button lets you fire off a question faster, without opening the full pane.
Helpful nudges — Contextual coach messages introduce people to Copilot and point them to the button, so more of your team discovers what it can do.
--about — A handy power-user command that tells you which model and version you’re using.

🔐 For admins & site owners

One model provider — Copilot in SharePoint now runs on the latest reasoning OpenAI model for everyone, without a fallback model. This means high quality answers and easier management.
Show/Hide the Copilot button per site — Site owners get a setting to disable the Copilot button for site visitors on a per-site basis — useful for governance on sensitive sites.

Try it today

Copilot in SharePoint is available for users with a Microsoft 365 Copilot license. For ready-to-use prompts and step-by-step guidance, explore the Copilot in SharePoint adoption hub and the Getting Started prompt library. If Copilot in SharePoint has not yet rollout to your tenant as on by default, your IT Admin can opt in to the preview following the instructions here.

Bring us your business challenge! Work directly with Microsoft Engineering to design and deploy a real Copilot in SharePoint solution for your organization. From knowledge management to custom skills and workflow automation, we'll help selected Microsoft 365 Copilot customers go from idea to production. For free. Nominate your scenario here.

See you next month for the next round of what’s new! 🚀

Can you preserve links when exporting from a list to Excel?

Lupusregina_Beta — Thu, 16 Jul 2026 13:48:09 GMT

Hi!

I have a list with a multiline text field set to support enchanced richtext used to store URLs of files. I am inserting data into this list when people submit a Jotform using Power Automate to connect the two. In Power Automate, I build the field value by looping the uploaded files and building out HTML:

Problem is, if I go to the list and do Export to Excel, the query.iqy file I get just shows file.png and file.pdf as basic, nonlinked text in the column. Is there any way to get Sharepoint to create an Excel file where the links are preserved?

Hero Web Part Background Image Is Not Resized After Display Scaling Change

SungYong — Thu, 16 Jul 2026 08:27:07 GMT

When the Display Scale under System > Display is changed from 100% to 125%, the Background Image size in SharePoint Web Parts > Hero > Slide is not automatically resized.


Display 125%	Display 100%

"labels" in the left navigation pane

Padoh — Thu, 16 Jul 2026 08:01:30 GMT

Hi, Can anyone advise me if it is possible to add labels in bulk to libraries? What I am asking badly is....I have over a hundred libraries on a site and they need labels adding to them so they are "grouped" in categories. Some of the labels need applying to several libraries. I have been doing smaller sites manually but the bigger ones will be very tedious and time consuming. I hope that makes sense?

Format Power BI report in SharePoint page

connorfelice — Wed, 15 Jul 2026 16:35:17 GMT

Hey everyone,

I have a wide and short Power BI report that is 2500 by 350 and I am trying to embed it in a SharePoint page. The problem is that when I embed it there is a ton of negative space below the report. I tried formatting the web part but there are only two display ratios (16:9 and 4:3). Is there a way to shorten the web part or reduce the negative space below the report.

SharePoint Online Site Deletion blocked by Retention Policy although no content is retained

FrankSch — Wed, 15 Jul 2026 09:15:38 GMT

Hi everyone,

I'm trying to understand whether this behavior is expected or if other organizations are experiencing the same issue.

Environment

SharePoint Online (Microsoft 365)

Microsoft Purview Retention Policy

Retention duration: 180 days

Trigger: based on last modified date

After retention period: Do nothing

The intention of the policy is not to automatically delete content. We only want to prevent deletion for 180 days and allow deletion afterwards.

Microsoft previously explained to us that retention applies to the content/items, not to the container (the SharePoint site itself).

Problem

We are currently unable to delete SharePoint sites.

When attempting to delete a site from the SharePoint Admin Center we receive the following message:

This site can't be deleted at this time because it is blocked by a compliance policy.

What we already checked

For the affected site:

CompliancePolicyIds = empty

ArchiveStatus = NotArchived

SensitivityLabel = empty

The Preservation Hold Library is empty:

Preservation Hold Library => Items = 0

We also inspected the internal DLC properties of the site and found entries such as:

dlc_sitehaspolicy

dlc_sitehasexpirationpolicy

dlc_webhasexpirationpolicy

Even after clearing these values for testing purposes, the site still could not be deleted.

Important Observation

If we explicitly exclude the site from the retention policy, deletion becomes possible.

This makes it appear that the retention policy is blocking deletion of the site container itself, even though our understanding was that retention should only protect retained content and not permanently prevent deletion of the entire SharePoint site.

Has anyone else observed this behavior?

Additional OneDrive Issue

We are seeing a similar problem with unlicensed OneDrive accounts.

Many of these OneDrive sites:

are already archived

are shown as blocked by retention/compliance

cannot be deleted

remain present for years

For example:

ArchiveStatus = FullyArchived

CompliancePolicyIds = empty

The accounts appear in the list of unlicensed OneDrive accounts, but we are unable to permanently remove them while the retention policy is in effect.

Has anyone found a practical way to clean up or remove these orphaned/unlicensed OneDrive sites when a tenant-wide retention policy is configured?

Questions

Has anyone seen a situation where:

CompliancePolicyIds is empty

the Preservation Hold Library is empty

but site deletion is still blocked by a compliance policy?

Does a retention policy configured as:

Retain for 180 days

Then Do nothing

intentionally block deletion of the entire SharePoint site container?

Is it expected that excluding a site from the retention policy immediately makes deletion possible?

How are other organizations handling site lifecycle management when tenant-wide retention policies are enabled?

Has anyone experienced similar issues with unlicensed OneDrive accounts that cannot be deleted because of retention/compliance requirements?

Any experiences, recommendations, or explanations would be greatly appreciated.

Thanks!

Recycle Bin and Site Content Hidden for Site Members and Site Visitors

Brendaama1 — Tue, 14 Jul 2026 21:52:58 GMT

I posted this before but made a mistake with my write up, so I am reposting.

I have a requirement that the Recycle bin and Site Contents should be be hidden from the Site Members and Site Visitors. Please how can I achieve this because the Server Infrastructure activation is not working for me. Any workarounds? Please help

Issues with SharePoint Product Catalog (Classic)

ChrisFicek — Tue, 14 Jul 2026 19:45:01 GMT

Is it a known issue for search-based web parts (content search web part or search box, refiners and results) to be interfering with a page layout integrated with catalog item reuse snippets in SharePoint Online (classic)?

Due to the limitation in creating a product catalog site in modern experience, we went down the path of creating a classic version of product catalog in SharePoint Online. For a year and a half, the product catalog has been working fine until recently while there were no new changes made to the page or the page layout. The page used to surface catalog item content based on the product tag in the URL (https://[tenantname].sharepoint.com/sites/products/overview.aspx?myproduct=apple) as well as search-based content.

Upon investigating the issues, it appears that the catalog item reuse snippets on the page layout and the search-based web parts both are interfering with each other's. If we removed search-based web parts from the page, the catalog item content then surfaces. If the search-based web parts are re-added, the catalog item content then disappears. Has anyone experienced this issue?

Can PnP Provisioning preserve SharePoint List GUIDs across environments?

rosamartinamilazzo25 — Tue, 14 Jul 2026 15:44:58 GMT

I am using PnP Framework 1.15.1 with the 2022/09 Provisioning Schema to provision SharePoint Online lists.

After applying the provisioning template, I noticed that all newly created lists have different SPList.Id (GUID) values compared to the source site.

My template contains standard pnp:ListInstance definitions, for example:

<pnp:ListInstance

Title="Event Types"

TemplateType="100"

Url="Lists/EventTypes"

TemplateFeatureID="00bfea71-de22-43b2-a848-c05709900100">

...

</pnp:ListInstance>

I checked the 2022/09 schema and I couldn't find any attribute to specify the list GUID.

I also noticed that the provisioning engine registers ListIdToken using createdList.Id, which seems to happen after the list has already been created.

My questions are:

Is it expected that PnP Provisioning always creates new SharePoint List GUIDs?

Is there any supported way to preserve the original SPList.Id during provisioning?

Was this ever supported in previous versions of the PnP Provisioning schema or framework?

Any official documentation or confirmation from the PnP team would be appreciated.

Recycle Bin and Site Content Hidden for Site Members and Site Visitors

Brendaama1 — Tue, 14 Jul 2026 14:44:21 GMT

I have a requirement that the Recycle bin and Site Contents should not be be hidden from the Site Members and Site Visitors. Please how can I achieve this because the Server Infrastructure activation is not working for me. Any workarounds? Please help