New collaborative agents in Microsoft 365 Copilot
Watch how these collaborative agents partner with you in real time across your everyday apps. Knowledge Agent streamlines SharePoint by auto-tagging files, retiring outdated pages, and even drafting new content so your sites stay current and searchable. Facilitator is an agent in Microsoft Teams that keeps meetings on track — managing agendas, taking notes, assigning follow-ups, and capturing decisions automatically. Agents in Teams channels summarize conversations, generate status reports, and handle routine updates so projects move forward without missed details. Agents in Viva Engage communities draft accurate, data-driven responses to questions, connecting colleagues to the right information and reducing response times. Organize and tag your SharePoint content automatically. Turn your library into a smart knowledge hub. See how to streamline SharePoint with Knowledge Agent. Keep your team aligned. Generate summaries, compare features, & automate status reports with channel agents. Check out how to use agents in Microsoft Teams channels. Run meetings efficiently. Track agendas, take notes, assign follow-ups, and stay on time. See how Facilitator can improve your meetings. QUICK LINKS: 00:00 — Collaborative agents in Microsoft 365 Copilot 01:08 — Knowledge Agent in SharePoint 02:29 — Keep SharePoint site up-to-date 03:41 — Create pages and new posts in SharePoint 04:47 — Agents in Microsoft Teams channels 06:34 — Facilitator in Microsoft Teams meetings 07:58 — Agents in Viva Engage communities 09:12 — Wrap up Link References Find out more at https://aka.ms/HumanAgentTeams Unfamiliar with Microsoft Mechanics? As Microsoft’s official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast Keep getting this insider knowledge, join us on social: Follow us on Twitter: https://twitter.com/MSFTMechanics Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics Video Transcript: -The latest agents for Microsoft 365 Copilot users can now act as virtual members of your team. Today, I’ll show you how new collaborative agents for Microsoft 365 Copilot can work alongside you in real time. Starting with the Knowledge Agent to help you manage your sites and organize your content in SharePoint. Then Facilitator in Microsoft Teams helps you run more effective meetings, working through your agenda and driving the right actions. Followed by the agents in Teams channels, which work with your team, answering questions and helping everyone stay on top of your projects, for example, generating regular status reports. -And, finally, the agents in communities which help shorten your response times with suggested and informed responses to scale your support in Viva Engage. And all of these agents are grounded in the context of your work: the meetings you’re invited to, the conversations you’re in, and the files that you have access to. And you can use these agents directly from your work-approved app. So you don’t need to move information to external AI tools, which puts your work data at risk. Let me show you how each of these agents work and how they can help you. I’m going to start in SharePoint because collaboration isn’t just about the meetings that you’re in or your work chats, it’s also about having the right information at the right time. -Now, this SharePoint document library is a bit disorganized. There are a lot of specs and decks and roadmaps all mixed together. And that’s where Knowledge Agent comes in. It can really turn your content into an active, self-organizing knowledge base. I’ll just pull up the agent and choose Organize This Library. And that takes a moment to process. And in these new columns that you see right here, it’s tagged the files with product, the matching department, and even the material type used to manufacture the products, as mentioned within each file. Now I have the contents in this library all tagged and almost how I want it, but I still need to organize the content for how our team works with it. So, for that, I can just ask Knowledge Agent to show the files for ZavaCoreFiber and then group them by department. And as it’s working, you’ll see that it groups everything as described. And this is the exact view that I want. This saves us all the time it takes then to add metadata to our files and keep our information sorted. And with everything as intended and our groupings in place, I’ll just save the changes and I’m done. -Next, if you’ve ever owned or worked in a long-running SharePoint site, they can become out-of-date quickly with old content or broken links. And guess what? Knowledge Agent helps there too. So I’ll open up Knowledge Agent, and this time I’ll choose Improve the Site. And it gives me a few suggestions right away, like retire inactive pages, find content gaps, and fix broken links. Now, these are all things that this site can use, to be honest. So I’ll go ahead and start with retiring inactive pages. Now, there are two different inactive pages, one from 2022 and one from 2018, that probably should be retired, and I can preview them right from this card. And since this one looks out-of-date and it’s due, I’ll go ahead and retire it. -Now let’s look for content gaps. And this is a great insight because people have searched for Zava Collab and related terms 35 times in the last year, but they didn’t find any relevant results. Let’s see if we can fix that. I’ll go ahead and click Suggestion, and Knowledge Agent recommends creating a dedicated Zava Collaboration page and even has recommended content ideas for it. And that’s another area where a Knowledge Agent can help you save time by creating pages and news posts in SharePoint. Let me show you that. This time from the floating menu, I’ll choose Create a Page. For the type, I’ll pick a news post. And as a template, I’ll select Create a Newsletter. Then I just need to define a few specific items to customize my news post. So I’ll specify the subject, ZavaCore Fiber, then I’ll go ahead and add a section for upcoming launch. And from there to reference the right knowledge needed to inform the news posts, I’ll go ahead and point to a presentation that I’ve been working on with the right details in it. -Now I can create this as a private draft. And, again, we can watch everything right now as it’s being created where it drafts out the content, even finds the right images, and applies the layout to the news post. Now after I go ahead and review everything, I just need to confirm and post the news. The Knowledge Agent turns your content into a living and learning knowledge partner that you can interact with in real time. But now let’s switch gears to the Teams channel that we use to converse with each other daily. -If you’ve ever been part of a busy or active Teams channel, it’s easy to lose track of decisions and deadlines, and that’s where agents and channels act as a project-knowledgeable teammate grounded in your channel’s content. So here I’m in our Teams CoreFiber-Launch channel. A member of the team is looking for a product feature comparison, and that’s a great idea. I’ll go ahead and head over to the threaded replies and call up our agent. I’ll just @ mention CoreFiber-Launch Agent. There we go. Now I’ll ask it to compare ZavaCore Fiber features to what’s in the market for smart clothing and produce a concise summary. And it generates the summary with all the details it found with grounding information from the web and from channel conversations. -And the agents can also work autonomously too. So, for example, if you want your agent to send a project status report using information from the team, you can set it up automatically to send updates. And all you need to do is go into the Agent Settings. And, in this case, we’ve already set up the agent to send daily status report updates as well as schedule meetings. And just to show you how that works, I’ll go ahead and edit the status report settings so that, starting on September 11th, it’ll start sending weekly status reports instead of daily. Just to give you an idea then of what these different status reports look like, I’ll head over to the Status Report channel, and you can see the components with the highlights and lowlights, the current status with details on progress towards our milestones, and more. -These agents in your Teams channels are like having teammates who remember everything and also take actions on your behalf. And while we’re in Teams, let’s move on to meetings and see what Facilitator can do. So this is an in-progress meeting that was just kicked off with recording and transcription enabled, and this is Serena’s view. So in Meeting Chat, like with other agents, you can @ mention Facilitator and pull it up. Now, in this meeting there are three agenda items that need to be discussed, and Facilitator can do that with a simple prompt to track meeting progress. -Now, instantly you’ll see the agenda appears right on top of the meeting stage, and Facilitator even sets timings for each agenda item in this 10-minute meeting. And it also offers to take notes and set a timer and also answer questions. In fact, Carole asked Facilitator to choose between New York, Chicago, San Francisco, and Atlanta for a product launch based on city size and other factors. Facilitator then responds with suggestions based on the size and strengths of each city. As everyone in the meeting moves through the topics, Facilitator keeps the meeting on track and even finds action items like this one to outline a platform strategy that it can assign as a task in Planner. -Once the meeting’s finished, Facilitator generates the meeting notes as discussed and follow-up tasks. And this agent ensures that nothing falls through the cracks. Then for those broader, often company-wide level conversations happening in Viva Engage communities, agents there can also help quickly answer questions and even connect people to the resources they’re looking for. So here I’m in Viva Engage in our Product Sales Support community, and I can see that a sales team member has asked “What are the KPIs, or key performance indicators, and success metrics that we’re tracking for the ZavaCore Fiber launch?” -Let’s go ahead and look at the suggested draft response from the agent. Now, as you can see here as I scroll down, the agent’s constantly looking for unanswered questions and automatically generates and suggests informed responses. And here’s our question on KPIs and success metrics. And you can see that it’s retrieved the right information right from our launch docs and our SharePoint site from before and was able to draft an informed response. Now all I need to do is review it and approve it. And that takes a moment to add the response. And, once that’s complete, it even gets marked as verified with a blue check because I reviewed it as an expert. -These agents make community knowledge and expertise more accessible and save lots of time, whether you’re waiting for the response or authoring the response. The new agents in Microsoft 365 Copilot act as virtual teammates. They help you stay informed, organized, and efficient so you can focus on what matters. -To learn more about these and other agents, check out aka.ms/HumanAgentTeams. Subscribe to Microsoft Mechanics for the latest AI updates. And thanks so much for watching.New Microsoft 365 Copilot Tuning | Create fine-tuned models to write like you do
Fine-tuning adds new skills to foundational models, simulating experience in the tasks you teach the model to do. This complements Retrieval Augmented Generation, which in real-time uses search to find related information, then add that to your prompts for context. Fine-tuning helps ensure that responses meet your quality expectations for specific repeatable tasks, without needing to be prompting expert. It’s great for drafting complex legal agreements, writing technical documentation, authoring medical papers, and more — using detailed, often lengthy precedent files along with what you teach the model. Using Copilot Studio, anyone can create and deploy these fine-tuned models to use with agents without data science or coding expertise. There, you can teach models using data labeling, ground them in your organization’s content — while keeping the information in-place and maintaining data security and access policies. The information contained in the task-specific models that you create stay private to your team and organization. Task-specific models and related information are only accessible to the people and departments you specify — and information is not merged into shared large language models or used for model training. Jeremy Chapman, Director on the Microsoft 365 product team, shows how this simple, zero-code approach helps the agents you build write and reason like your experts — delivering high-quality, detailed responses. Keep information permissions as-is. Use your organization’s knowledge and sharing controls. See how Copilot Tuning works. Guide Copilot with labeled examples. Copilot learns to reason and write like you are your expert team. Check it out. Build Copilot agents powered by your fine-tuned models. Automate work with your tone, structure, and standards. Take a look at Copilot Chat. QUICK LINKS: 00:00 — Fine-tune Copilot 01:21 — Tailor Copilot for specialized tasks 05:12 — How it works 05:57 — Create a task-specific model 07:43 — Data labeling 08:59 — Build agents that use your fine-tuned model 11:42 — Wrap up Link References Check out https://aka.ms/FineTuningCopilot Unfamiliar with Microsoft Mechanics? As Microsoft’s official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast Keep getting this insider knowledge, join us on social: Follow us on Twitter: https://twitter.com/MSFTMechanics Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics Video Transcript: -You can now teach or fine-tune your Microsoft 365 Copilot experience by creating your own task-specific fine-tune models that channel your expertise and experience to carry out specialized jobs and tasks accurately and on your behalf. In fact, from Copilot Studio, anyone can use this zero-code approach to teaching Copilot’s underlying model the skills from your organization to produce more usable, high-quality responses that can be as detailed as they need to be, even hundreds of pages long to get the job done. And the model remains exclusive to your organization and only the people and departments you specify. -If you compare this to the traditional way of doing this until now, this level of customization would require data science, machine learning, and coding skills. So this process is a lot simpler. And unlike existing approaches where, as a data scientist, you may be copying data into locations that may not be aware of your protections and access controls, this is enterprise-grade by design. You just focus on the outcome that you want to achieve. And because your data stays in place, your existing data access and protection policies are respected by default. Let me show you the power of this in action by comparing the results of an agent that’s calling a fine-tuned, task-specific model of Copilot versus one that’s just calling the original underlying Copilot model. So both agents are configured to author loan agreement documents. On the left is our agent using the task-specific model, on the right is our SharePoint-based agent using a general model. -Now, both agents are focused on the same exact underlying knowledge. It’s all in a SharePoint location, as you can see here with this precedent file set. And both user prompts are identical with example reference files and the client term sheets containing new information. In fact, this is a precedent file that I’ll use. It’s a long and detailed document with 14 pages and more than 5,000 words. The term sheet is quite a bit shorter as you can see here, but it’s still long and detailed with information about the loan amounts, all the details, and if I scroll all the way down to the bottom, you’ll see signatory information for both parties. -So let’s go back to our side-by-side view and run them. So, I’ll start with the general model agent on the right. And it starts to generate its response. And I’ll let this one respond for a moment until it completes. There we go. And now I’ll move over to the agent on the left. It immediately informs me that it’ll receive an email once it’s finished. Now, this is going to be a longer-form document, so we’ll fast forward in time to see each completed response. -So, starting with the general model, I’ve copied it into a Word document, and the output is solid. You’ll see that the two parties are correct, the loan structure, all the amounts are also correct from the term sheet, but it has a few tells. It’s missing a lot of specificity and nuance that a member of our legal team would typically include in all of the terms. It’s also very summarized and not how our firm would draft an agreement like this. When I scroll down to the bottom, the signatories and addresses are captured correctly and match the term sheet. That said, though, it’s just four pages long and has around 800 words, versus more than 5,000 words in our precedent document. So it kind of follows the 80–20 rule where a good portion of the response could maybe work with some edits, but it’s not reflecting how my firm thinks and how it writes when authoring legal documents like this one. -So let’s go ahead and look at the results of a fine-tuned, task-specific agent. So immediately, you can see this document is verbose. It’s 14 pages long with more than 5,300 words. The word count doesn’t always equate to quality, so let’s look at the document itself. Now, as I scroll down, you’ll see that this agent has been taught our firm-specific patterns and the clauses that we use in existing case files. It is structured and worded things just like the precedent document. It’s reasoning and writing with more precision, like an experienced member of our firm would. And while as with any other AI-generated document, I still need to check it for accuracy, it really captures that extra detail and polish to save us time and effort. So model fine-tuning is a powerful way to tailor state-of-the-art large language models that are used behind Copilot to your specific needs. -And as you saw, it also can significantly improve the handling of specialized tasks. So let me explain how fine-tuning works in this case. Unlike Retrieval Augmented Generation, it doesn’t rely on search and orchestration processes that run external to the large language model. The additional knowledge added as part of the fine-tuning process is a protected container of information that attaches the large language models training set to teach it effectively a new skill. Now, it’s never merged into the LLM or used for future model training, and is temporarily attached to the LLM when it’s needed. Again, the skill and knowledge that it contains is exclusive to you and the people or groups that you’ve shared it with, so it can’t be accessed without the right permissions. -Next, let me show you what it takes to create and fine-tune your own task-specific model. I’m in Microsoft Copilot Studio, which you can reach from your browser by navigating to copilotstudio.microsoft.com. I’m on the task-specific model page and I want to customize a model to generate partner agreements. So I’ll paste in a corresponding name. Then I’ll paste in a description. Then as the task type, I’ll select a customization recipe that reflects what I want it to do. And my options here include expert Q&A, document generation, and document summarization, with more task types coming over time. From there, I can provide additional instructions to tailor the fine-tuning recipe, like how the model should use original files, for example, to inform the structure, formatting, company-specific clauses, and other areas important to your model, like we saw before. -Next, I can define my own knowledge sources. Now, these can use information from SharePoint sites and folders, and soon, you’ll be able to add information external to Microsoft 365 using Microsoft Graph connectors. In this case, I’ll define a SharePoint source. Then browse the sites that I have access to. I’ll choose this folder inside the Agreements library. And from there, I can even drill into specific folders for the precise information that I want to use to teach the model, which I’ll do here with the Agreements folder. -For permissions, this process aligns to the enterprise-grade controls that you already have in your organization backed by your Microsoft Entra account. Now, the next step is to process the data you selected for training or what’s known as data labeling. So here, you’ll be presented with data labeling tasks in small, iterative batches. They’re kind of like questionnaires for you to complete, where the fine-tuning process will generate documents and request assessment of them for clarity, completeness, accuracy, and professionalism. This process requires subject matter expertise to open these documents and rate the quality of the generative output for each. I’m just going to show one question here, but you’d repeat this process for every batch. And once all batches are labeled, I can start model training. Now, this will take some time to process, so I’ll fast forward a little in time. -Now with everything finished, I can publish the model to my Microsoft 365 tenant. And it will be available to anyone we’ve shared it with, like our audit team from before, to build new agents. And the process I just showed is called supervised learning, where the model is trained on label data. And soon, you’ll also have the option to use reinforcement learning to enhance the agent’s reasoning capabilities. Now let me show you how to build an agent from Copilot Chat that can leverage our new task-specific model for partner agreement generation. So I’m going to select Create agent. And for the purpose, I have a new option here to build a task-specific agent. Next, I can choose from the existing task-specific models. So I’m going to choose the one that we just created for new partner agreements. There we go. And with any agent, I just need to give it a name. Now I’ll paste in a description for people on the team to know its purpose and what it can do. -And next, I can specify additional instructions as guidelines to provide more context to the agent, as I’m doing here to ensure the structure aligns with our organizational standards. Because this is a very specific agent to write partner agreements, I’ll just specify one starter prompt with details for referencing a precedent source document to start with and a term sheet to get specific new information from, kind of like we saw before. Now, the preview on the right looks good, and I can create the agent right from here. For sharing, permissions also need to align with whoever my task-specific model was shared with, which, as you’ll remember, again, was our audit team. In this case, for my own validation, I’ll select only you so that I can test it before sharing it out with other auditors on my team. -So let’s go ahead and test it out. So I’m going to use the starter prompt. Then I’ll replace the variable file names here. I’ll use the forward slash reference, starting with the precedent file. Now I’ll look for the term sheet file. There it is. From there I can submit my prompt. This is going to take a moment for the response. You can see the structure with sections based on our task-specific files used with the fine-tuning. It tells me that it’ll send me a Word document and email once it’s finished again. In fact, if I fast forward in time a little, I’ll move over to Outlook. And this is the file the agent sent me with links to the new agreement draft. So I’ll open it using Word in the browser. There’s my agreement. And you’ll see it follows exactly how we wrote the precedent agreement. As I scroll through the document, I can see all the structure and phrasing aligned with how we write these types of agreements. In fact, this Representations and Warranties section is word for word direct from our standard terms that our firm always incorporates. And that’s it. My agent is now backed with my task-specific, fine-tuned knowledge, and it’s ready to go and I’m ready to share it with my team. -So those are just a few examples of how fine-tuning in Microsoft 365 Copilot can give you on-demand expertise, and task-specific models respond more accurately using your specified voice and process so that you and your team can get more done. -To find out more, check out aka.ms/FineTuningCopilot, and keep watching Microsoft Mechanics for the latest tech updates, subscribe to our channel, and thanks for watching.
