Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 492: Microsoft Copilot’s New Agents: Insider tips how to make them work for you

Episode Date: March 28, 2025

How did no one notice these AI Agents? 🤯 While we’ve all been busy playing with GPT-4o photos and checking Gemini 2.5 benchmarks, Microsoft legit dropped some of the most useful AI agents we’ve seen. But how do you use them? And how do they work? If only you could get the answers from…… say Ray Smith, the VP of AI Agents at Microsoft?Oh wait, you can. Join us as we go over Microsoft Copilot’s brand new agents: and the insider tips on how to make them work for you.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the conversation and ask Jordan and Ray questionsUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:Microsoft Copilot's New AI AgentsInsider Tips on Leveraging AI AgentsMicrosoft VP Ray Smith's InsightsMicrosoft Copilot Studio OverviewDeep Reasoning Capabilities in CopilotNew Agent Flows in Copilot StudioUsage-Based Pricing in Copilot StudioNatural Language as Primary Programming LanguageRole of AI Agents in BusinessesDetailed Functionality of Deep Reasoning AgentsIntegration of Agent Flows in Business ProcessesTimestamps:00:00 Microsoft Copilot's New AI Agents07:00 Getting Started with M365 Copilot10:43 Research Agents: Building Blocks for Apps14:47 Dynamic Use Cases in Business Development17:50 AI's Transformative Impact21:11 Enterprise Agent Governance Essentials24:06 "Integrating Automation with Copilot Studio"27:59 "Hands-On AI Business Transformation"Keywords:AI Development, Microsoft Copilot, AI Agents, Everyday AI Show, Livestream Podcast, Daily Newsletter, Large Language Models, youreverydayai.com, Deep Reasoning Capabilities, Agent Flows, Autonomous Agents, Copilot Studio, Natural Language, RFP Generation, Sales Development, Enterprise Grade Controls, Azure Subscription, Usage-Based Pricing, Power Automate, Reinforcement Learning, Research Agents, Code Generation, Report Generation, Financial Analysis, Multi-Agent Scenario, Business Process, Deterministic Behavior, Generative AI, Experiential Learning, IT Integration.Microsoft Copilot’s New Agents: Insider tips how to make them work for youSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. It's been one of those weeks in AI development where, you know, you're like, did all of this

just happen over the course of a couple of days? It seems like all of the big players have offered something new for us all to take advantage of this week. And I think Microsoft maybe had some of the biggest announcements that I don't think enough people are talking about because this is. tools now, new AI agents that we can all use. So today I'm excited to talk about and have a great guest on the show. Returning guests, by the way, to talk about Microsoft co-pilots, new agents and

give you some true insider tips on how to make them actually work for you. All right, I'm excited for today's conversation. Hope you are too. What's going on, y'all? If you're new here, my name's Jordan Wilson, and this is the Everyday AI show. This thing is for you. It's your daily live stream podcast and free daily newsletter, helping us not all just keep up with AI, but how we can use all these new tools and all these new advancements and new large language models to get ahead to grow our companies and our careers.

So it starts right here by listening to this live stream flash podcast. This is where you learn. But to leverage it, you need to go to your everyday AI.com. Sign up for the free daily newsletter. We're going to be breaking down the most important takeover. ways from today's conversation as well as giving you a lot more information that you need to know to actually take advantage of what we're going over today. So make sure you go do that at your everyday AI.com. All right. If you're looking for the daily news, that's going to be in the

newsletter as well. We got to take advantage of every second we can with today's guests. So please help me welcome to the show. Let's bring them on. There we go. Ray Smith, the VP of AI agents at Microsoft. Ray, thank you so much for coming back a second time to join the everyday AI show. Thanks for having me back, Jordan. Clearly, the first time went, okay, if that's why you have me back on. So I'm glad to be here. Yeah, and like, obviously, right, like, if you can talk to the VP of AI agents at Microsoft,

like, we got to have the conversation. So many people are asking me, you know, hey, Jordan, what's new with all these agents in Microsoft? So I said, like, all right, let's ask, let's ask the man himself. So, Ray, what the heck is new? I mean, there's so much new inside co-pilot studio with these new AI agents, but walk us through some of the hot off the presses announcements. Yeah, I mean, think when we last spoke back, I think it was back in November,

it was around the ignite time frame. There was lots of releases. And as you touched on, it just seems week after week. Things are just moving fast. And I don't see it slowing down. So, you know, this is going to be week after week. There's going to be feature releases. There's going to be new capabilities.

And actually, I think we're at this new era where the customer engagement, the real production use cases is pushing the tooling across the full agentic stack more and more. I think this week we announced like deep reasoning capabilities to really kind of bring that kind of analysis, research, report generation into typical workflows. We also kind of announced around agent flows, which is a new way of bringing kind of what would have been kind of traditional RPA, but bringing some of these guardrails and real, real deterministic behavior as a key. tool into how we build these agents. So this mix of deterministic and non-deterministic. So we asked the agent to reason over the tooling, we'll maybe choose its path. And for certain parts of that path,

we always wanted to behave the same. So that's kind of agent flows. And actually what we also announces that the autonomous agent's capabilities has gone to general availability. So that's in the last couple of days. So yeah, so lots going on. And I think when we last spoke, I think we were talking about 100,000 organizations using co-pilot studio. That's now at 160,000. And there's like, I think the status, like over 400,000 agents were built in the last three months alone. So we're, I would say we're still at early innings, but it's only accelerating. And the best part of my day is meeting customers. And they're like, do you think you could do this and it could do that? And unlike technology is no longer the barrier,

it really is the focus on these use cases. And we'll see across every level of the agenetic stack, new capabilities around testing and evaluation frameworks, new tools, improvements around rag and orchestration. But the net net is just for end users and for everyday people is, it's going to be easier and easier to build these apps in this new world. The biggest program in language is going to be just natural language. It's not going to be the code.

that we are all familiar with. It's just going to be describing what you want, and you're going to interact and iterate to build these solutions. So it's a very exciting time for business, leaders, domain experts to say, hey, I don't need to take a ticket with IT or I can work more easily with IT, at least to build these solutions quicker. And if those two things right there that you just heard from Ray didn't excite you, right?

So obviously everyday AI, you know, we're for non-technical people. Those two things. Technology no longer being the barrier. And the most important kind of programming language now is natural language. Those two things, I'm like, I'm excited to dive in more. But before we do, I first want to zoom out a little bit, Ray. So you know, you said now more than 160,000 organizations using Copilot Studio. Amazing. But for those that started using Copilot Studio first, let's just give everyone a quick overview. What the heck is Copilot Studio? How do you access it? And then I'm really excited to talk about these two new agents. Yeah, so it's go to co-pilot studio.com and you can set up a trial and get started pretty easily.

What it is is effective. It's a low-code agent or app building in this new AI world, framework or solution. So it's trying to abstract away all the complexities of model selection frameworks, how we add tools, how we kind of bring knowledge sources together. So all with kind of enterprise grade controls, observability, and governance. So it's trying to bring this enterprise grade solution building or agent building framework, but also make it really kind of easily for the average, you know, kind of almost business user to describe what they want and to iterate through that process. And I know it's even access has changed a little bit.

But is this available, you know, if everyone has, you know, Microsoft 365, you know, seats for everyone in their organization. Is this available to them? And then second part, if it's not, can they still use the, you know, kind of the pay-as-you-go pricing to take advantage of these two new agents in co-pilot studio? Yeah, so there's probably a number of ways. Obviously, first of all, it's easy to go in and just get a trial set up. But in terms of beyond that trial or beyond whatever it is, 30 days, then it's really a case off. There's a number of ways. Either you buy MG-65 co-pilot licenses and you get certain entitlements. or you put in your Azure subscription and it draws down against your Azure commitments.

Or you prepay and buy effectively message packs or kind of by packs that draws down on that meter. So we're trying to make it even easier for people to get started. Because in this new world, it really does take people just get hands on with the tools. It's like you build your first agent, then you get bitten by the bug and you're like, oh, I'm going to build the next 10. And the first one is always the hardest because it's the kind of the content. concepts around how rag and how you use actions and connectors and triggers to make it autonomous. But after you kind of get that first one, then it's kind of, I get the conversation with customers.

It's like, we heard what you said. We liked it. But then when we built our own, the penny really dropped. And we now are just like we wanted like a gentic transformation or AI first transformation in their businesses. Yeah. And I do think the usage based pricing was super smart. because, yeah, I even remember walking around, you know, talking to people at Microsoft Ignite. And I'm like, why, you know, for people that aren't using this, why.

And, you know, at the time, people were like, oh, you know, we're not sure if we want to, you know, roll out hundreds or thousands of licenses. So I think there was a smart move, by the way. But let's get into the good stuff, Ray. Let's talk about the deep reasoning agent. Like, like, what the heck is this? I'm excited about it. Yeah. So foundationally, these new class of models are based on kind of reinforcement learning.

And really what it is, that difference is that it's like a model that can kind of verify itself and be trained based on the output. So it's kind of like we call it think deeper or kind of like think longer or kind of self-analyzed. So it's like it's able to look at itself these models based on the output. So if you think about that, it has to be a verifiable output. So when you are creating code or creating a kind of an analysis or some sort of research or generating a report, it's something that can be easily evaluated compared to. just, let's say, flowery language. So that's, it's a different type of model. And it's all based in that reinforcement learning. And, you know, we're familiar with Open AI. So they had 01, then 03,

mini and pro. So there's just different flavors of it. There's obviously deep seek. So we think there's going to be a number of these reinforcement learning or deep reasoning models that will emerge. And obviously, our view of Microsoft is to make sure we make all of these different models accessible to our users, both in Azure AI Foundry, and also when in Copilot Studio, because ultimately it would be around picking the right model for the right job. That's optimized. In the fullness of time,

customers will fine-tune those models for their own use cases. So that's fundamentally what powers it. So you have these deep reasoning models. On top of that, people are like, okay, that's cool. You give me a model. What can I do with it? And they usually start to use it almost like an action. Say this part of my process,

where I've gotten all this information from the web, or I've pulled all this information from sharepoint and some content. Now I'm going to give it over to this model and it's going to behave better than the standard orchestration models, let's say, 4.5 in OpenAI's case. So it'll kind of reason or think deeper across that information. So they start to use it more like an action. And this is very useful if you can think about like, you know, inventory optimization or research about a lead or a company as it comes through a sales development workflow. So this becomes a key step. And I think what a lot of we're hearing about in the market is people are abstracting another level of building these apps or research agents, which is really packaging up the research models, maybe access to the web or certain tools.

And it's allowing people to say, I want to use this building block, either to generate a report or a response to an RFP, doing all the analysis. or maybe I'll use that as a building block into a multi-agents scenario such as, let's say, sales development, where you say, I want you to go off and research this leader, this company, so that it can be used by the next step in the process. So, you know, fundamentally deep reasoning or, you know, research agents or analysis agents are almost a fundamental building block for most processes, because that's the thing we as humans do really well, which is reason over a lot of complex information,

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You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adobie.com. So yeah, great, great call out there on the Think Deeper. Like I think, you know, Microsoft even made a lot of that available for free. So, you know, I did cover that in episode, I think, 479. So if you want to know more about that, make sure to go check that out. But, but Ray, like I think.

one thing people are going to have questions about or just be curious about right now when we see this thing you know deep research or deep reasoning right like we think of that certain you know brand like i think open ai's deep research product one of the uh like for me personally like i love using it so is that kind of like what this is this microsoft's version of it how is it the same how is it different yeah so uh probably foundationally they're using similar models or as i said we can make models interchangeable But 01 and 03 are, you know, the kind of the best deep reasoning models in certain scenarios, particularly when we're kind of connecting to your enterprise data. As I said, it's that kind of app is just the wrapping or the kind of the packaging up of how you access that model, what tools, whether it's using code interpreter or ability to create code, search the web, to do deeper analysis backed by code, particularly if you're doing financial analysis. So the research agent is just a way of packaging all of that open to a simple interface or natural language, hey, can you help me with this task?

And it goes off and does it. That's basically it. So a very, very similar concept to the research agents or any kind of analysis agent in the market. So give us an example, right? Because, well, first, I think the deep reasoning agent is available now in co-pilot studio. But, you know, give us an example. Like how should someone be, you know, maybe using this and what capabilities in this new deep reasoning agent in Copilot Studio? What new capabilities are there by using it that maybe weren't available before it?

Yeah. So I think the, it really comes down to these use cases. And as I said, like we've a number of customers live in production, pushing, you know, scenarios. And the kind of research requirement, particularly in that kind of business development, sales development use cases, became very clear early on around kind of lead scoring, lead qualification, lead research. But in other scenarios where it's like engagement management, where requests for information or request for proposals will come in,

there'll be a complex project plan, there'd be requirements from the customer saying, you need to meet these conditions, these costs, these timelines, these criteria. So complex number of variables for people to kind of go, how am I going to process this? today the answer or thus far the answer was hand that over to a human to look at all the kind of constraints all the variables all the information and to you know create maybe a an or a p or a proposal at the end of that so today this is a great use case where you would give that over to a deep

reasoning agent or deep reasoning model back to it like an orchestration around that and it would intake from let's say the email so it automatically triggered from an email that says, hey, I'd like a proposal for this product, this many units by this date and, you know, my pricing agreements. And it would, from that email, it would automatically trigger, go through a whole process to generate the RFP. It may or may not have a human in the loop to say, hey, here's the proposal I've built.

And so you can see how that would dramatically kind of make that process more efficient and ultimately actually help you kind of maybe sell more because you're kind of on top of these, these proposals. So I think that's just one example, or FPE or sales development as an example, but I think the ones that we typically see is either kind of in the coding space, in the report generation, it's really good at generating reports. So when you say, I want a report on this, and from the code gen, where it's doing financial analysis, where it may be backed by some code that it generates on the fly to do this analysis and bring back those results to maybe take an

action on that in a downstream system. So, you know, one other kind of question that I had on this is, you know, with this, you know, deep reasoning, and I love that example, it's, you know, taking these things that would normally be multiple, you know, human checkpoints, you know, maybe, hey, does this fit our criteria for a project? Yeah, right? like going through multiple of these steps. How does this change kind of the human role in all of this, right?

Like I know we always talk about like human in the loop, right? Like I like to say like expert or expertise in the loop. But for those individuals and companies that are going to start leveraging this deep reasoning agent in co-pilot studio, how does this change kind of even their role? Yeah, I think it's, I think. will struggle to see any kind of industry, any role, any department not to be influenced or impacted by this kind of agentic transformation of disruption. So I think we as humans are like, we should learn how to harness the power of these capabilities to be more efficient, more effective in the

roles that we're in. And I think we see that across, you know, a number of industries, number of use cases. And fundamentally, it's going to shift from we as humans having to do as the front line of doing some of this more boring and mundane work that oftentimes we don't want to do to having that delegated to an AI agent that will maybe just come to me with the research and the pre-brief before I jump on the call with the customer as an example or before I do some MNA acquisition or an M&A process where I'm like I've done the risk analysis it's poured over the deal room as an example so it's it's maybe either bring into a human or it's it's augmenting a part of a process that we will be doing today.

But it could also be fully autonomous in certain circumstances. But we as humans will be always overseeing these agents, handling exceptions, if it's unable to proceed or doesn't want to proceed because it thinks it doesn't have enough information or, you know, you've codified it to say, don't proceed beyond this point if you think the refund is beyond this point or if there's some sort of risk management that you kind to bake into these agents. So therefore, you'll have people in department saying, hey, I'm processing invoices, but the agent is doing 90% of the invoices, maybe 95%, and I'm there to handle the exceptions where it couldn't pull out key information from the documents or whatever it may be,

as an example. So I think we will see a shift to how we can leverage AI more and how we will shift from, you know, individual contributor to kind of managing agents more across our typical processes. And I'm assuming one other, you know, big difference, right? And let's just pick an easy one to compare to, right? So if you're using, you know, chat GPTs, you know, deep research, or you're using chat GPTs, you know, 0103, something like that, the big difference with the deep reasoning agent in Copilot Studio is it can access your dynamic data. inside Microsoft 365 is number one, like, can it access all your data? Like what, you know, I guess like what other tools or apps does it have access to?

And what does that unlock in terms of capabilities? Yeah, that's, it's a good point that you make there, Jordan, because like fundamentally, we as humans set up these agents and how they operate, whether it's like what other agents that can talk to, what connectors, what systems, what knowledge sources. we configure it, we build these agents the way we see fit. And it's very similar to how we kind of set up roles within departments, say you're in the finance department, you have access to these tools, these knowledge sources or these SharePoint sites.

So very similarly, we as humans will provision and make these agents with total control over what they can and cannot access. So that's number one. Number two is really, it's like even when we have these agents and we've tested it and we debugged it and we kind of deployed it. We're confident around how reliable it is. We're going to want to oversee it. So that kind of governance, observability, and all those kind of security requirements are going to be essential, I would say, because not just when you've got one or two or three agents, but like we're going to have hundreds of thousands of agents

across our business, across departments. They're going to be various agents aligned to one department versus another, all rolling up to the business leaders that run, run those teams and run those departments. And I think, you know, I would say that interbraise grade kind of connectivity to various systems and tracking all those connections. Similarly, when you bring knowledge into these agents, we shouldn't just have a drag a file from your local desktop. And then, by the way, when I share this agent with everyone that got access to everything

that's grounded in that agent, we at runtime want to be able to check as like, you know, does this user have access to this document or this file? also sensitivity labels, the access controls. So all of these things, I would say, is a thing that Microsoft, being in the space for so long, is kind of like differentiated on or it kind of focuses on around that kind of enterprise knowledge, connectivity, and security and governance. Obviously, all the innovative breakthroughs and the various tools from, you know, COWA, operator, CodeGen, you know, all of these things we've just even talked to it today,

which is, you know, agent flows and deep reasoning. They're essential building block tools, but if you don't kind of secure and kind of have a platform that is reliable, then all the tools don't really matter. So let's talk a little bit about agent flows. I believe that should be rolling out to everyone March 31st. Correct me if I'm wrong on that. Yeah, I think it's Monday, yeah.

Yeah, there we go. So, I mean, what is this and how does this, you know, change what, like what's possible? And we'll be sure to share the little video in our newsletter. I think watching that really helps. But maybe just for our podcast audience, just describe, you know, agent flows and how this changes, you know, really this agentic workflow. Yeah. So we've been on a journey over the last number of decades to just automate more across

their business, whether that was kind of scripts, macros, in Excel, or write programs. And then obviously the last decade we've had kind of a low code automation. So, you know, building automation. or P.A. So robotic process automation to automate key parts in our business. And that, you know, that has been a hugely successful business, lots of businesses. And at Microsoft, we've got a tooling called power automate. What we've learned in this agentic revolution was, yes, they want lots of agentic reasoning. So there's reasoning brain at the top of a process where it looks across the various tools. But there's certain times where you really want

deterministic behavior. You want the tool to run. from A to B each and every time. And that's where you will leverage automations or you'll leverage connectors into existing systems. And you don't want necessarily too much variability in that. So bringing agent flows and our power automate capabilities natively into Copilot Studio is we're bringing this healthy mix of deterministic outcomes and, you know,

setting kind of well-defined paths through a process that won't change over time. So whether you're generating code or, creating a prescriptive workflow, that's where agent flows really comes in. So it's kind of like two sides to the same coin of how we will do or complete a business process. Some parts will be reasoning or even deep reasoning, deciding what tools to use.

And sometimes one of those tools will just be, it's an automation. And that automation, we can use LLMs or use AI to help create those automations. We can even bring in kind of prompts and reasoning, elements into the prescriptive part, but it's a key use case that unlocks more kind of control. And that's what we're seeing from our customers is that kind of mix between the two is a, is a, is a good mix.

And I can put myself in the position to some people right now. And they're hearing some of these terms, right? Like, oh, power automate and deterministic RPA agent flows. But like, I go back to what you said earlier. It's like, well, all you really need for all of this is natural. language, right? Can you just quickly walk people through like, hey, like, technically you don't need to be, you know, have a decade of experience in power automate or something like that to take advantage of agent flow. Yeah, this is a great point, Jordan. And like, it's something where we're seeing this level of abstraction from all the different tooling on the need. And it should just be, and even with agent flows, you describe what you want. And it will build the flow for you. And obviously, you verify, you tested and you may iterate back and forth and say, no, no, instead of this step, I want you to. do this. So natural language we see as the language for generating solutions. And more and more,

we're going to move that like input up the stack where you don't even decide what tool you want to use. You described it. And the agent itself will decide, I think you're looking for a prompt here, Ray, or maybe we'll create an automation here at this point. Or maybe, do you know what, that's cool. We're going to move a mouse around a VM because you talk to it some legacy app. So it will abstract more and more away. And I think that's what you'll see in the tooling. it will get to the point where it's like you and I having a conversation here, we'll describe the problem, we'll iterate through it, and behind the scenes, it's picking the tools,

it's either generating code or automations on the flight. I mean, just the amount of capabilities and, you know, opening this up to even non-technical people, I think it's just a really exciting time in generative AI. So, you know, Ray, we've talked about a lot in this conversation, but, you know, from everything that's new with agents in co-pilot studio, the deep reasoning agent, the agent flows, right? But as we wrap up, maybe what is your one most important kind of takeaway or tip on how

business leaders can get these to work for them today? Yeah, I'm glad you asked this one because this is something I usually finish most customer engagements with. It is kind of overwhelming how everything is moving fast. It's like all this technology. And I think the market has kind of woken up that this is going to be transformative. There's huge ROI and there's lots of case studies out there. and weekly everyone's publicizing how they're saving lots of money,

be more efficient or ways to generate revenue using agents. I think that can be overwhelming as well because you're like, I'll build a spreadsheet, 200 use cases. It's got a subtotal to millions or billions of dollars and we're going to change the change that we run our business. First and foremost is you've got to get your hands on the tools. And I see this across the board. It's like pick a use case.

You pick a business process. You break that up on the parts. And you're going to be like, I'm going to build an agent for this part. I might have a human either side to verify and kind of work that into the process. And slowly, slowly but surely, you will automate the whole process or large parts of it by building a series of agents that will chain together. So my kind of advice is you just get on, get your hands on the tooling. Because it really is a kind of a experiential learning of just figuring out how these AI tools work.

What are the, how do you put guardrails around the tooling that you use and so on? And as we said, it's getting easier and easier with more obstruction. But in the early days, it really requires just hands-on experience and a use case focus. All right. This was a great session, right? We can close class for the day. I learned a lot and I know that our audience did too. So, Ray, thank you so much for joining the everyday AI show to walk us through what's new with co-pilots, new agents.

We really appreciate it. Cheers. Thanks, Jordan. All right. And as a reminder, y'all, we covered a lot. If you missed anything, we're going to be sharing a lot of other links and resources to everything that Ray just walked us through. So if you haven't already, please make sure you go sign up for our free daily newsletter at your everyday AI.com.

So thank you for tuning in. Hope to see you back for more Everyday AI. Thanks, y'all. Meet Firefly AI assistant. Now live in Adobe Firefly, the Allman One Creative AI Studio. Just describe what you want to create in your own words and the assistant handles the rest. orchestrating multi-step workflows across Adobe Creative Cloud apps, including Photoshop, Premiere Express, and more in one conversational interface.

You direct the outcome while the assistant accelerates execution. Stand control with the ability to step in and refine at any time. See it today at firefly.adobie.com. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit Your EverydayAI.com and sign up to our daily newsletter so you don't get left behind.

Go break some barriers and we'll see you next time.