Reading Progress in Microsoft Teams to improve student reading fluency - now rolled out globally!
Reading Progress is a free tool designed to support educators in creating personalized reading experiences that build confidence and reading fluency in their students. Starting today, Reading Progress is beginning global rollout! Expect about two weeks for this tool to be available everywhere.
Token Limit Exceeded? What's Actually Going On and What to Do About It ?
Hi All, Based on some recent experience across the organisation with token limit issues, I wanted to put my thoughts down and actually dig into what's happening under the hood, rather than just chalking it up to "we need a bigger plan." If you work anywhere near the Microsoft ecosystem these days, you're probably touching more AI tools than you realize. Copilot in Word and Excel, GitHub Copilot while you code, Copilot Studio if you're building agents, maybe Security Copilot or Copilot for Sales depending on your role, and increasingly Azure AI Foundry if your team is building anything custom. I work across a good chunk of this stack day to day, and at some point, almost everyone runs into the same wall: "Token limit exceeded." "You've reached your usage limit." "Upgrade to continue." The first instinct is usually to assume you did something wrong wrote too much, uploaded too big a file, or just need a fatter subscription. Sometimes that's the actual story. But honestly, often, that error message is standing in for three completely different problems that all happen to look identical from the outside. One is about how much text a model can physically process at once. One is about your license or credits running dry. And one has nothing to do with size at all it's just about how fast you're sending requests. Once you know which of these three, you're dealing with, the fix becomes obvious. Until then, "upgrade your plan" feels like the only lever you've got even when it isn't. This post walks through what a token is, why Microsoft's various Copilots each handle this differently, and what habits genuinely cut down on these interruptions instead of just throwing money at the problem. Part 1: So What Is a Token, Really? A token isn't a word, and it isn't a character it's somewhere in between. It's the small chunk of text a model's tokenizer breaks your input into before it can do anything with it. Take a word like "unbelievable." A tokenizer might split it into three pieces something like "un," "believ," and "able." Short, everyday words usually come out as a single token. But code, technical jargon, acronyms, and non-English text tend to fragment into a lot more tokens than you'd guess just by looking at the word count. This is why every AI tool has a ceiling on how much it can handle in one go, and that ceiling isn't measured in words or characters it's measured in tokens. Your prompt, any documents or emails it pulls in as context, the back-and-forth history of your conversation, and the response itself all draw from the same pool. Once that pool runs dry, something has to give: the tool truncates, rejects the request outright, or quietly summarizes older context to make room. The part that trips people up: token count doesn't map cleanly to word count. A short, dense paragraph full of code or acronyms can eat up more tokens than a much longer plain-English message. Part 2: Three Different Limits, One Confusing Error Message This isn't always obvious upfront, even to a lot of admins managing these tools: "token limit exceeded" is really a stand-in phrase for three separate limits, and they don't behave the same way. This isn't unique to Microsoft either every major AI platform bundles these same three things behind similarly vague error messages. Microsoft's stack just makes a good case study because so many of us touch multiple pieces of it in the same week. The context window is the ceiling on how much text a specific model can process in a single request everything from your prompt to retrieved documents to chat history. This is tied to the model itself, not your subscription. Swap from one model to another inside the same tool, and this ceiling can move without you doing anything differently. Your license, credits, or feature allowance is a completely separate thing. This is what Microsoft 365 Copilot plans track through AI credits and feature limits, and it's what Copilot Studio measures through Copilot credits at the environment level. A single action summarizing an inbox, generating an agent response, running an analysis deducts from this pool regardless of how small your actual prompt felt. Run out, and you get blocked, even if you're nowhere near any context window limit. The rate limit is about speed, not size. Copilot Studio, for instance, enforces quotas measured in requests per minute or per hour to keep the system stable under load. Send messages too quickly, which happens easily with automations, flows, or bots, and you can get throttled even with a tiny prompt and plenty of credits left. The reason this matters: a plan upgrade only ever fixes the second one. If you're actually running into the model's context window or getting rate-limited, paying for a bigger license won't change anything, and that mismatch is exactly where most of the frustration comes from. Part 3: How This Plays Out Across the Microsoft AI Stack The Microsoft ecosystem isn't one AI tool wearing different outfits it's genuinely several different systems, each handling tokens and limits in its own way. Here's a tour of the ones people run into most. Microsoft 365 Copilot (the one living inside Word, Excel, Outlook, Teams) doesn't work off a single published token number the way a developer tool would. Instead, it dynamically pulls together your prompt, recent chat history, and relevant snippets retrieved from Microsoft Graph your files, emails, and messages and quietly summarizes or drops older material to stay within bounds. Where this usually breaks isn't the context window at all; it's the AI credit and feature-limit system running out, often without much warning until you're mid-task. GitHub Copilot Chat is more like a traditional developer tool. It has a fixed, published token window tied to whichever model you've selected, and that limit applies consistently whether you're in the browser, VS Code, or the CLI. The failure mode here is usually a long conversation or a big multi-file context quietly creeping past that ceiling. Copilot Studio, where a lot of custom agent-building happens, runs on Copilot credits per interaction, plus its own requests-per-minute and requests-per-hour quotas at the environment level. If you're grounding an agent in SharePoint content, there's also a separate file-size ceiling to watch content over a certain size can get silently excluded from generative answers depending on your tenant's licensing. Azure AI Foundry (recently renamed to Microsoft Foundry, in case you've seen both names floating around) is where this gets more directly in your control. If your team is building custom applications on top of Azure OpenAI or other models in the Foundry catalog, which now includes everything from GPT to Phi to Claude to Llama, you're working with explicit, published context windows per model, and you're billed per token rather than per credit. It's a different mental model entirely: less "you hit a wall," more "you're paying by the word, so design accordingly." Security Copilot, if your org uses it for threat analysis and incident response, runs on its own capacity model pooled compute units at the tenant level rather than a simple per-user cap. It's easy to assume this behaves like M365 Copilot license limits; it doesn't. Copilot for Sales, embedded in Outlook and Teams for CRM-connected work, and Copilot in Power BI, which now goes beyond generating summaries to actually helping build and refine semantic models, both draw from their own feature-specific allowances layered on top of whatever base Microsoft 365 or Power Platform license you're on. And then there's the multi-model wrinkle that trips up teams the most: because tools like Copilot Studio and GitHub Copilot let you choose between GPT-based models, Claude, and others, the exact same prompt can have a different effective context window and a different token cost purely based on which model handled it that day. This is a big, underrated reason behind the "it worked fine yesterday, why not now" complaint. Part 4: What Actually Helps ? Some of this is genuinely outside your control, but a fair amount isn't. If you're just using these tools day to day, the single biggest habit shift is not letting conversations run forever. Long threads in Copilot Chat or Copilot Studio keep accumulating history, and that history eats into the same budget as whatever you're asking right now. Starting fresh periodically costs you nothing and buys back a lot of headroom. Large documents are worth splitting up before you feed them in, especially for SharePoint-grounded agents, where oversized files can get quietly excluded rather than cleanly rejected you won't necessarily know it happened unless you're looking for it. And it's worth resisting the urge to default to the heaviest, most capable model for every single task. Lighter models are usually faster, cheaper, and often sit under a more generous limit than the flagship ones, and most everyday tasks genuinely don't need the biggest model available. Before you go asking IT for a license upgrade, it's worth a quick sanity check on which limit you actually hit. If it's a rate limit, waiting a minute and retrying usually solves it outright. If it's a context window problem, trimming your prompt or starting a new session fixes it. An upgrade only helps if you've genuinely run out of credits or feature allowance, and that's worth confirming before you file the request. If you're on the building side Copilot Studio agents, Foundry applications, anything with RAG-style grounding a couple of things pay off quickly. Keep an eye on credit or token consumption proactively rather than discovering it's gone when the agent goes down mid-conversation. Be deliberate about what goes into system prompts and orchestration instructions, since those draw from the same budget as the end user's actual message, often invisibly to whoever's chatting with the agent. And spend real time getting chunk size right for knowledge sources too large and you're burning budget on irrelevant context, too small and the agent loses the thread. Part 5: Quick Checklist Before You Escalate Is this actually a context window problem -prompt, history, and attachments too big for the model in use? Have you genuinely run out of credits or feature allowance on your plan? Could this be a rate limit -too many requests too fast, especially from a flow or automation? Did the underlying model change since last time, quietly shifting the effective window? For Studio or Foundry work, is this a tenant or environment-level limit rather than something tied to you personally? Closing Thoughts Tokenization is one of those things that stays completely invisible right up until it isn't. Across a stack as sprawling as Microsoft's M365 Copilot, GitHub Copilot, Copilot Studio, Foundry, Security Copilot, and everything layered on top "token limit exceeded" almost never means one single thing. It means you've hit one of three very different walls, and each one needs a different response. If your team builds or maintains any of these tools, this is genuinely worth putting in front of people early. Most of the "why did this break" tickets in this space aren't about tokens at all. They're about nobody knowing which limit actually got hit, or where in this increasingly large ecosystem it happened. I'm curious how this shows up for others has your team standardized on one model across these tools, or are you juggling several depending on the task? I'd love to hear what patterns you've run into. Cheers, and happy reading. - By Surya Vennapusa, MCTAccess fixes released in Version 2604 (Build 16.0.19929.20090)
Bug Name Issue Fixed Values display in the wrong control when using a form as a sublist When a form was used as a sublist (subdatasheet), field values could display in the wrong control, showing data in incorrect positions. Values now display in the correct controls. Applications that use the Access Database Engine (ACEOLEDB) terminate unexpectedly on exit Third-party applications using the Access Database Engine (ACEOLEDB) provider could terminate unexpectedly when closing. The shutdown sequence has been corrected. Long Text field corrupted when a query updates a record while a user is editing it When a query updated a Long Text field on a record that was simultaneously open for editing, the field data could become corrupted. The record update now correctly handles concurrent access to Long Text fields. Rendering errors with Aptos (Detail) font Controls using the Aptos (Detail) font variant could render incorrectly, with characters appearing misaligned or garbled. The font rendering has been corrected. Standard colors in Access didn't match other Office apps The standard color palette in Access used different color values than other Office applications like Word and Excel. The color palette has been updated to match the rest of Office. Option Group with Vertical Anchor Bottom: option buttons show incorrect visual state after clicking When an option group control had its Vertical Anchor property set to Bottom, clicking an option button would not correctly update the visual state of the buttons. The visual state now updates correctly regardless of the anchor setting. Query Design: Insert/Delete Columns don't work when ribbon is set to Show Tabs Only In Query Design view, the Insert Columns and Delete Columns commands on the ribbon did not work when the ribbon display option was set to "Show Tabs Only." The commands now work correctly regardless of ribbon display mode. SQL View: Ctrl+K should toggle pretty formatting off/on In the Monaco SQL editor, the Ctrl+K keyboard shortcut did not toggle SQL formatting. Ctrl+K now correctly toggles pretty formatting on and off. Monaco editor incorrectly converts Unicode characters in SQL view When switching between Design View and SQL View, the Monaco SQL editor could incorrectly convert certain Unicode characters, corrupting the SQL text. Unicode characters are now preserved correctly. Importing text files with Unicode characters in the filename fails Attempting to import a text file whose filename contained certain Unicode characters would fail. File imports now handle Unicode filenames correctly. Added VarP and StDevP to the Totals query aggregate dropdown The VarP (population variance) and StDevP (population standard deviation) aggregate functions were missing from the Totals row dropdown in Query Design view. They have been added alongside the existing Var and StDev options. Added VarP and StDevP to the datasheet totals row dropdown The VarP and StDevP aggregate functions were missing from the Totals row dropdown in Datasheet view. They have been added to match the options available in Query Design view. Access hangs at shutdown when VBA holds temporary DAO field references Access could hang during shutdown when VBA code created temporary DAO field references. The shutdown process now correctly cleans up temporary field references. Full Screen Mode ribbon display option does nothing in Access Selecting "Full Screen Mode" from the ribbon display options had no effect in Access. This option now works correctly, hiding the ribbon to maximize the available workspace.Cross Tenant Mailbox Migration: NotAcceptedDomainException
This week I'm performing a new cross tenant mailbox migration. I have some experience with this kind of migrations, ( it's the third one I'm in charge of ), and with the new procedure, ( will paste the link with the instructions at the end of this article ), an Azure Key Vault is no longer required, so I was very confident and thought that I would no have any issue. But, as sometimes occurs, I was wrong The setup was quite easy, and the mail users configuration was like always, so no a big deal. But now comes the point... Once I launched the migration batch, half of the users started syncing correctly and the ther ones failed, ( neither a MoveRequest was able to start for them ). Once I checked the errors, I got the same for all the failed ones: " NotAcceptedDomainException: You can't use the domain because it's not an accepted domain for your organization ". Ok. No problem... ( I thought ). I work with Exchange since more than 10 years and this is a common error message. ( Again I was wrong ). I started to check the mail users, looking for some misspelled domain, missing alias, spaces, etc... Basically, the troubleshooting for this kind of errors. But from my perspective all looked good. So, I decided to reconfigure all the mailusers with a script, launch a delta sync, and resume the failed moverequest. But again, same error for all of them. Checked again, with PS, from source and target tenant, checked in AD, all the proxy addresses... Nothing, all was correct! Non sense... Ok. At that point I decid to compare some syncing mail users with some failed ones, looking for anything that could be a pattern. And "voilá"! The syncing users were all licensed in O365... The failed ones not! After assigning a license to the failed ones and resume the MoveRequest, all started to work smoothly. For sure, I would have saved many hours of work if the error message had been: " The user is not licensed ". But, yeah... It would have been too simple 🙂 Summarizing, make sure that the mail users have an O365 license before you start the migration batch. And remember, not always the error messages are what they seems to be 🙂 Cross Tenant Mailbox Migration procedure, ( Preview 😞 https://docs.microsoft.com/en-us/microsoft-365/enterprise/cross-tenant-mailbox-migration?view=o365-worldwideAnnouncing new H5P and OneNote integration to help bring interactive content to life
As we continue listening to students and educators, we have heard many requests for OneNote integration with H5P, the tool that allows people to create, share and reuse interactive content. Today we are excited to announce the integration between H5P and OneNote.
Frustration with Microsoft’s Task Ecosystem
Why is planner development so slow. Is missing no brainer features that a 12 year old program manager or junior developer would even know what is needed at a very basic level. I’m not going to mention what these features are because this community is full of posts about this. You killed off ToDo which was an incredibly good app and had huge potential now left rotten away. Despite no development for years it’s still a great app and would be up there with the best of them - Todoist, Tick Tick, Apple Reminders, Things. Don’t get me started on Loop. What is the point of it. Planner in Teams is still slow. you have ruined Microsoft Project trying to get it integrated with Planner. Planner licensing is a mess. Planner agent currently doesn’t see premium projects. I know it’s coming. M365 Copilot can only see the default Task list in ToDo but no other list. Who thought that one up? You allow people create multiple lists in Todo but ignore them for Copilot query. I would post a really good convo with Copilot about all of this and even Copilot is scratching its head as to why Microsoft is so crap offering up half baked apps and then drop them for something else thus creating an ecosystem that barely functions and leaves people so frustrated. I could go on but I won’t. Rant over but no wonder people are opting to move to dedicated applications like Asana or Monday.com or Todoist for stuff like this. 🥸🤔😳🤦♂️🤷♂️💋How can you stay competitive and relevant in an AI-Driven World?
In a world where AI tools evolve weekly and yesterday's skills can feel obsolete overnight, this blog offers a grounded, human-first guide for cloud and technology professionals who want to stay ahead not by chasing every trend, but by building the right foundations. Across six core themes, the post walks readers through understanding what AI truly changes in the workplace, committing to deliberate and structured learning through platforms like Microsoft Learn, getting hands-on with real Azure AI projects beyond just certifications, and doubling down on the human skills critical thinking, communication, and ethical judgment that AI simply cannot replicate. The blog also makes the case for community and network as a long-term career asset, and closes with a call to develop an AI mindset rooted in curiosity, adaptability, and a willingness to experiment and share openly. Whether you're a cloud architect, a security professional preparing for AZ-500 or SC-200, or simply someone navigating what this AI shift means for your career this post is written for you. Key Takeaways for Readers: Understand AI's real impact · Build a deliberate learning habit · Go hands-on with Azure AI tools · Strengthen human skills · Invest in community · Cultivate an AI-first mindset
