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Unlocking New Potentials with Microsoft Copilot’s Enhanced AI Agents
In a week filled with groundbreaking developments in the AI sector, Microsoft has quietly made strides that demand closer attention. The introduction of new AI agents within Microsoft Copilot Studio promises to greatly augment business operations by making complex tasks more accessible and efficient. Here's how these advancements can be leveraged to drive business success.
Deep Reasoning Agents: Enhanced Decision-Making
Deep reasoning capabilities have been introduced, designed to revolutionize workflows by providing sophisticated analysis and report generation. Using reinforcement learning, these models excel at processing complex information, offering nuanced solutions. This development is particularly beneficial for tasks like inventory optimization and sales development, where understanding and evaluating numerous data points is critical. With these tools, business leaders can expect sharper insights and better outcomes.
Agent Flows: Streamlined Process Automation
Agent flows, set to be available to all users by late March, integrate seamlessly with Power Automate's functionalities. This new feature allows businesses to dictate deterministic behaviors within workflows, ensuring specific processes remain consistent with every iteration. By simplifying automation through natural language instructions, companies can maintain control over certain tasks while enjoying the benefits of AI-driven decision-making in others.
Simplifying AI with Natural Language
One of the key takeaways from the latest Copilot updates is the emphasis on natural language as the primary tool for interacting with AI systems. As these technologies move towards understanding and executing tasks based simply on descriptive language, the barrier to entry for nontechnical users is significantly reduced. This allows more employees at all levels to develop and manage AI agents without needing deep technical expertise, accelerating the pace of AI adoption across firms.
Applying AI in Real-World Business Scenarios
Consider scenarios like RFP management, where the deep reasoning capabilities can autonomously generate proposals by analyzing complex requirements. Similarly, in sales development, these agents can evaluate leads and automate follow-ups, resulting in enhanced efficiencies. By delegating routine, data-heavy processes to AI, human workers can focus on strategic tasks, leading to improved productivity and job satisfaction.
Hands-On Experience: The First Step to AI Integration
To truly benefit from these advancements, business leaders are encouraged to engage directly with the tools. Even a single use case can offer tangible insights into the capabilities of AI agents and lay the groundwork for broader integration. With Microsoft offering multiple access avenues, from trial setups to subscription models, there has never been a better opportunity to explore these tools.
In conclusion, the updates to Microsoft Copilot Studio bring advanced AI capabilities into the realm of everyday business operations, making once-complex tasks manageable through intuitive interactions. Business owners and leaders should capitalize on these new agents to enhance decision-making, streamline operations, and ultimately foster a more innovative workplace. To keep up with the ever-evolving landscape of AI, familiarity and hands-on experience with these tools are indispensable.
Topics Covered in This Episode:
- Microsoft Copilot's New AI Agents
- Insider Tips on Leveraging AI Agents
- Microsoft VP Ray Smith's Insights
- Microsoft Copilot Studio Overview
- Deep Reasoning Capabilities in Copilot
- New Agent Flows in Copilot Studio
- Usage-Based Pricing in Copilot Studio
- Natural Language as Primary Programming Language
- Role of AI Agents in Businesses
- Detailed Functionality of Deep Reasoning Agents
- Integration of Agent Flows in Business Processes
Podcast Transcript
Jordan Wilson [00:00:15]:
It's been a 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 guest, by the way, to talk about Microsoft Copilot's new agents and give you some true insider tips on how to make them actually work for you. Alright. I'm excited for today's conversation. Hope you are too. What's going on y'all? If you're new here, my name is Jordan Wilson, and this is the Everyday AI Show. This thing, it's for you.
Jordan Wilson [00:01:10]:
It's your daily livestream podcast and free daily newsletter, helping us on 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 youreverydayai.com. Sign up for the free daily newsletter. We're gonna be breaking down the most important, takeaways 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 youreverydayai.com. Alright.
Jordan Wilson [00:01:51]:
If you're looking for the the the daily news, that's gonna be in the newsletter as well. We gotta take advantage of every second we can with today's guest. So, please help me welcome to the show. Let's bring him on. There we go. Ray Smith, the VP of AI agents at Microsoft. Ray, thank you so much for coming back, second time to join the Everyday AI show.
Ray Smith [00:02:13]:
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.
Jordan Wilson [00:02:19]:
Yeah. And and, like, obviously, right, and, like, if you can talk to the VP of AI agents at Microsoft, like, we gotta 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, alright. Let's ask let's ask the man himself. So
Ray Smith [00:02:33]:
Yeah.
Jordan Wilson [00:02:34]:
Ray, what the heck is new? I mean, there's so much new, inside Copilot Studio with these new AI agents, but walk us through, some of the hot off the presses announcements.
Ray Smith [00:02:43]:
Yeah. And, I mean, think, when we last spoke back, I think it was back in November, it was around the night 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 I I you know, this is going to be week after week. There's gonna be feature releases. There's gonna be new capabilities. And, actually, I think we're at this new era where the customer engagements, the real production use cases is pushing the tooling, across the full agentic stack more and more.
Ray Smith [00:03:12]:
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 or PA, 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 nondeterministic. So we ask the agent to reason over the tooling, but 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 announced is that that the autonomous agent's capabilities has gone to general availability. So that's in the last couple of days. So, yeah.
Ray Smith [00:03:58]:
So lots going on. And I think when we last spoke, I think we were talking about a hundred thousand organizations using, Copilot Studio. That's now at a 60,000. 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 do you think you could do this and could do that? And I'm like, technology is no longer the barrier. It really is the focus on these use cases, and we'll see across every level of the agentic 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 gonna be easier and easier to build these apps in this new world.
Ray Smith [00:04:49]:
The biggest program and language is gonna be just natural language. It's not gonna be the code that we, are all familiar with. It's just gonna be describing what you want, and, you're gonna interact and iterate to build these solutions. So it's a it's a very exciting time for, you know, business leaders, domain experts to say, hey. I don't need to take a ticket with IT and or or I can work more easily with IT at least to build these solutions quicker.
Jordan Wilson [00:05:13]:
Mhmm. And if those two things right there that you just heard from Ray didn't excite you. Right? Like, so, you know, obviously, everyday AI, you know, we're for nontechnical people. Those two things, technology no longer being the barrier, and the the the most important kind of programming language now is natural language. Those two things, I'm like, I'm excited to to dive in more. So but before we do, I first wanna zoom out a little bit, Ray. So, you you know, you said now more than a 60,000 organizations using Copilot Studio. Yeah.
Jordan Wilson [00:05:43]:
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.
Ray Smith [00:05:55]:
Yeah. So let's go to copilotstudio.com, and you could 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 kinda enterprise grade controls, observability, and governance. So it's it's, you know, 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.
Jordan Wilson [00:06:39]:
And I know, it's even access has changed a little bit. But is this available, You know, if everyone has, you know, Microsoft three sixty five, 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 Copilot Studio?
Ray Smith [00:07:00]:
Yeah. So there's there's probably a number of ways. Obviously, first of all, it you know, easy to go in and just get a a trial set up. But in terms of beyond that trial or beyond that whatever it is, thirty days, then it's really a case of there's a number of ways. Either you buy m three sixty five Copilot 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 a kind of a a buy packs that draws down on that meter. So there's 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 it's like you build your first agent, then you get bitten by the bug, and you're like, oh, I'm gonna build the next 10.
Ray Smith [00:07:45]:
And the first one is always the hardest because it's the the kind of the concepts around how rag and how you use actions and connectors and, and and and triggers to make it autonomous. But after you kind of get that first one, then it's, it's kind of, I I get the 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, agentic transformation or AI first transformation in their businesses.
Jordan Wilson [00:08:13]:
Yeah. And and I do think that usage based pricing was super smart because, yeah, I even remember walking around, know, talking to people at Microsoft Ignite, and I'm like, why you know, for 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 wanna, you know, roll out hundreds or thousands of licenses. So I think that 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.
Ray Smith [00:08:40]:
Yeah. So foundationally, these new class of models are are based on kind of reinforcement learning. And really what it is that difference is 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 think deeper or kind of like think longer or kind of self analyze. 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 the kind of, an analysis or some sort of research or generating 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 on that reinforcement learning.
Ray Smith [00:09:23]:
And, you know, we're familiar with OpenAI, so they had o one, then o three, mini and pro. So there's just different flavors of it. There's obviously DeepSeq. So that we think there's gonna be a number of these, reinforcement learning or deep reasoning models, that will emerge. And, obviously, our view of Microsoft is is to make sure that we make all of these different models accessible to our users, both in Azure AI Foundry and also with a Copilot Studio, because ultimately, it'll be around picking the right model for the right job, you know, that's optimized. And 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.
Ray Smith [00:10:03]:
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, at 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 gonna give it over to this model, and it's gonna behave better than the the standard orchestration models, let's say four zero or four five in OpenAI's case. So it'll it'll kinda 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 and our our company as it comes through a sales development workflow.
Ray Smith [00:10:43]:
So this becomes a key step. And I think what a lot of we're hearing about in the organ you know, in the market is people are obstructing 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, 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, and variables and make decisions downstream based on it.
Jordan Wilson [00:11:46]:
Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Gen AI. Hey. This is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and NVIDIA have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use GenAI. So whether you're looking for chat g p t training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to your everydayai.com/partner to get in contact with our team, or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GenAI.
Jordan Wilson [00:12:50]:
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 I did cover that in, episode, I think, four seventy nine. So if you wanna know more about that, make sure to go check that out. But but, Ray, like, I I I think one thing, you know, people are are are gonna 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 OpenAI's, deep research product, one of the, like, for me personally, like, I love using it.
Jordan Wilson [00:13:25]:
So is that kind of, like, what this is? Is this Microsoft's version of it? How is it the same? How is it different?
Ray Smith [00:13:31]:
Yeah. So, probably foundationally, they're using, similar models or, as I said, we can make models interchangeable. But o one and o three are, you know, the kind of the best, deep reasoning models, in certain scenarios, particularly when we're kinda connecting to your enterprise data. As I said, it's that kinda app. It's just the it's the wrapping or the kind of the packaging 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 with 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.
Ray Smith [00:14:15]:
That's basically it. So a very, very similar concept, to the to the research agents or any kind of analysis agent, in the market.
Jordan Wilson [00:14:23]:
So give us give us an example. Right, because, well, first, I think the the deep reasoning agent is available now in Copilot Studio. But, you know, give us an example. Like, how should someone be, you know, maybe using this and 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?
Ray Smith [00:14:47]:
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 case was became very clear early on around kinda, lead scoring, lead qualification, lead verse research. But in other scenarios where it's like engagement management where request for information or request for proposals will come in to be a complex project plan, there'd be requirements from the the customer saying you need to meet these conditions, these costs, these timelines, these criteria. So complex, number of variables for people to kinda go, how am I gonna 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 a an ORFPR 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 orchestration around that. And it would intake from, let's say, the email. So it'd be automatically triggered from an email that says, hey.
Ray Smith [00:16:01]:
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 on the loop, to say, hey. Here's the proposal I 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, RFP or sales development as an example. But I think the ones that we typically see is either kinda in the coding space, in the report generation. It's really good at generating reports.
Ray Smith [00:16:44]:
So when you say, hey. 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, to do this analysis and bring the back those results to maybe take an action on that in a downstream system.
Jordan Wilson [00:17:02]:
So, you know, one one other kind of question that I I I had on this is, you know, with this, you know, deep reasoning, and I I 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 the 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 for those, individuals and companies that are gonna start leveraging this deep reasoning agent in Copilot Studio, how does this change kind of even their role?
Ray Smith [00:17:50]:
Yeah. I think it's, I I I think we'll struggle to see any kind of industry, any role, any department not to be influenced or impacted by this kind of agentic transformation or 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 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 gonna shift from we as humans having to do as the frontline 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 m and a, acquisition or an m and 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 bringing to a human or it's augmenting a a part of a process that we will be doing today. But it could also be fully autonomous in in in certain sir circumstances.
Ray Smith [00:18:59]:
But we will 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 if there's some sort of risk management that you kinda bake into these agents. So, therefore, you'll have people in departments 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, from from the documents or whatever it may be as an example. So I think 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.
Jordan Wilson [00:19:54]:
And and I'm assuming one other, you know, big difference. Right? And let's just pick an easy one to compare it to. Right? So if you're using, you know, ChatGPTs, you know, Deep Research or you're using ChatGPTs, you know, o one, o three, something like that. The big difference with the deep reasoning agent in Copilot Studio is it can access your dynamic data inside Microsoft three sixty five. Is number one, like, can it access all your data? Like, what, you know, I guess, like, what other, you know, tools or apps does it have access to, and and and what does that unlock in terms of capabilities?
Ray Smith [00:20:32]:
Yeah. That's, that's a it's a good point that you you make there, Jordan, because, like, fundamentally, we as humans set up these agents on how they operate, whether it's, like, what other agents it 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 it's 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 and these these knowledge sources or these SharePoint sites. So very similarly, we will we as humans will provision and make these agents, with total control over what they can and cannot access.
Ray Smith [00:21:11]:
So that's that's number one. Number two is really it's like, even when we have these agents and we've tested it and we've debugged it and we've 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 gonna be essential, I would say. Because not just when you've got one or two or three agents, but, like, we're gonna have hundreds if not thousands of agents across, our our business, across departments. They're gonna be depart various agents aligned to one department versus another, all rolling up to, to the business leaders that run, run those teams and run those departments. And I think, you know, I I would say that enterprise 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, with everyone that got access to everything that's grounded in that agent, we at runtime want to be able to check because, like, you know, does this user have access to this document or this file, so sensitivity labels, the access controls? So all of these things is, I would say, is a thing that Microsoft being in the space for so long is, is kind of, like, differentiated on or kind of, is it, you know, focuses on around that kind of enterprise knowledge, connectivity, and security governance.
Ray Smith [00:22:35]:
Obviously, all the innovative breakthroughs and the various tools, from, you know, Kula operator code, 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 kinda have a platform, that is, that is reliable, then all the tools don't really matter.
Jordan Wilson [00:23:01]:
So let's talk a little bit about agent flows. I believe that should, be be rolling out to everyone March 31. Correct me if I'm wrong on that.
Ray Smith [00:23:08]:
Yeah. I think it's Monday. Yeah.
Jordan Wilson [00:23:09]:
Yeah. There we go. So, I I I mean, what is this and and how does this, you know, change what pot like, what's possible? And we'll be sure to, share the, the little video in our newsletter. I I think, you know, watching that really helps. But maybe just for our podcast audience, just just describe, you know, agent flows and and how this changes, you you know, really this agentic workflow.
Ray Smith [00:23:30]:
Yeah. So we've been on a journey over the last number of decades to just automate more across our business, whether that was kinda scripts, macros in Excel, or write programs. And then, obviously, the last decade, we've had a kind of a low code automation. So, you know, building automations or or PA, 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.
Ray Smith [00:24:06]:
So this 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 a well defined paths through our process that, that won't change over time. So whether you're generating code or creating a a prescriptive workflow, that's where agent flows really comes in. So it's kinda 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.
Ray Smith [00:25:06]:
And and that automation, we can, you know, 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, 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 is is a is a is a is a good mix.
Jordan Wilson [00:25:30]:
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 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 a decade of experience in empower automator or something like that to take advantage of agent flow.
Ray Smith [00:25:57]:
This yeah. This is a great point, Jordan. And and, like, it's something where we're seeing this level of abstraction from all the different tooling underneath. And 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 test it, 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 is the as the language, for generating solutions.
Ray Smith [00:26:23]:
And more and more, we're gonna move that, like, input up the stack where you don't even decide what tool you wanna 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 gonna move a mouse around the VM because you talked with 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.
Ray Smith [00:26:53]:
And behind the scenes, it's picking the tools. It's either generating code or automations on the fly.
Jordan Wilson [00:26:58]:
I mean, just just the amount of capabilities and and, you know, opening this up to even nontechnical people, I think it's just a really exciting time in generative AI. So, you know, Ray Ray, we've talked about a lot in this conversation, but, you know, you know, from everything that's new with with agents and Copilot 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?
Ray Smith [00:27:26]:
Yeah. I I'm glad you asked this one because this is something I usually finish most customer engagements with. It is kinda overwhelming how everything is moving fast. It's like all this technology. And, I think the market has kinda woken up that this is gonna be transformative. There's huge ROI, and there's lots of case studies out there. And, you know, weekly, everyone's publicizing how they're saving lots of money, being more efficient, or, you know, 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.
Ray Smith [00:27:59]:
It's got a subtotal to, you know, millions or billions of dollars, and we're gonna change the change how we run our business. First and foremost is you gotta get your hands on the tools. Right? And I see this across the board. It's like pick a use case, you pick a business process, You break that up into parts, and you're gonna be like, I'm gonna build an agent for this part. I might have a human either side to verify and kinda work that into the process. And slow lowly 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, and because it's 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 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.
Jordan Wilson [00:28:55]:
Alright. This was a a great session. Right? We can we can close class for the day. I I 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 Copilot's new agents. We really appreciate it.
Ray Smith [00:29:11]:
Cheers. Thanks, Jordan.
Jordan Wilson [00:29:12]:
Alright. And as a reminder, y'all, we covered a lot. If you missed anything, we're gonna 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 youreverydayai.com. So thank you for tuning in. Hope to see you back for more everyday AI. Thanks, y'all. And