AI Agents Solutions - IBM
https://www.ibm.com/solutions/ai-agents
Agentic AI Engineering: Complete 4-Hour Workshop feat. MCP, CrewAI and OpenAI Agents SDK
4 hour workshop
Jon Krohn
https://www.youtube.com/watch?v=LSk5KaEGVk4
YouTube Video
Artificial Intelligence Innovation Summit by The SolutionPeople Network
22 Aug 2025
Fist Presentation
Jordan Wilson, Your Everyday AI Podcast Host
"Agentic AI, Where we’re at, Where we’re going, And the biggest mistakes to avoid along the way "
Connect with Jordan on Linkedin at:
https://www.linkedin.com/in/jordanwilson04/
https://www.youtube.com/watch?v=SNkJBf7j0pE
To be edited and summarized.
Our first speaker in number one is Jordan Wilson from Your Everyday AI and his topic is Agentic AI, where we're at, where we're going, and the biggest mistakes to avoid along the way.
Jordan:
Agents are everywhere. I think it's important to maybe uh start this kind of series off by doing a little bit of a check on where we're at with Agentic AI, what it even is, where we're going, and if you're a business leader, what you should be focusing on, and the three biggest mistakes to avoid along the way.
Let's be honest. Are you guys seeing these things everywhere? These AI agents, right? Uh these these custom GPT agents and N8 agents and in Zapier, right? Uh so these are very powerful solutions, right? Custom GPTs and N8N agents and Zapier agents, but they're not actually agents. Uh they're agentic at times. But I think it's important to understand what an AI agent actually is since that's the thing right now that really has all of our focus.
8:12
We're in this almost agent washing epidemic. So, a recent Gardner study
revealed this and gave a kind of shocking truth about agentic AI and it's
8:25
essentially turned into a gold rush. So, what this Gartner study did is it looked
8:30
at 3,000 different vendors who were selling or promoting or built AI agents.
8:38
And they found that out of those 3,000, only 130 of them were actually AI
8:46
agents, were truly AI agents. And so what that means, more than 95% of them
8:52
weren't even agents. So, we have this epidemic now of just everything you see,
8:58
everything you use, things that used to just be a button on a website is suddenly rebranded as an AI agent. And
9:06
it's causing a lot of confusion. Yet, even though the majority of things being
9:12
marketed right now as agents are not agents, the real agentic wave is coming.
So a recent IDC and Microsoft study said that in three years there will be more
than 1.3 billion agents active in the enterprise.
9:32
All right? So these aren't uh pilot projects. Uh these aren't small business
9:37
entrepreneurs. This is more than a billion AI agents that will be out doing
9:43
work autonomously in the enterprise. So, this leaves us at uh a weird spot,
9:51
right? So, you might be hearing me say, "Hey, everything that's being talked about and hyped as an agent isn't an
9:57
agent." Yet, agents are actually coming. So, we're at this crossroads. All right.
10:02
So, over the next uh 15 to 20ish minutes, uh this is the road map that I
10:08
want to go through with you all together. So, number one, I want to talk about why the definition crisis of AI
10:14
agents is actually paralyzing your AI strategy. We're going to go over what an
10:20
AI agent actually is and what it isn't and also how to avoid the three biggest
10:27
mistakes of agentic AI adoption. All right, real quick about me because
10:33
you might be confused. Why is this guy talking about AI agents? Uh, so my name is Jordan Wilson. I run a daily, yes, a
10:40
daily AI podcast called Everyday AI. So, for the past two and a half years, um
10:46
this is what I do. Every single day, I talk, I get up, I get to interview very smart people. Um and it's our our reach
10:53
has grown uh significantly over the last uh few years since we've been doing this. So, I've been lucky enough uh to
11:01
get to speak with some of the smartest people in the world who are literally building AI. So, you know, getting to
11:08
meet people at conferences, but also getting to talk to uh the literally the people who are building agents, right?
11:15
Like from the uh VP of agents at Microsoft to seuite leaders at Google to
11:22
all of the startups building it uh to dozens of Fortune 500 CEOs who are
11:27
trying to implement AI. Um, I've been lucky enough to get to talk to and meet and really extract a lot of information
11:33
from some of the smartest people in the world and then I kind of share uh their secrets on the Everyday AI podcast. But
11:41
uh my background is a little bit more uh than just you know running a top 15 AI podcast. So I've been lucky enough to uh
11:48
train now that numbers uh up to like 15,000 uh people on prompt engineering
11:54
uh consulting for large enterprise companies such as GE healthcare and actually advising companies like Gartner
12:01
and including Gartner um on Agentic AI. So let's get back to that exact study.
12:08
And I think it's worth reiterating this because when I started my little
12:14
presentation here, I asked you all, how many of you have seen these agents, right? And uh I'll admit it, that was a
12:21
little bit of a trick uh because I knew those weren't agents. Yet it's so hard
12:26
um especially if you are a business leader or if you are leading AI implementation efforts at your
12:32
organization. It is extremely difficult to understand what's real, what's fake,
12:39
what's smoking mirrors, what's happening now and what's happening next. Um and what you'll see is it's agent washing.
12:47
every every feature, every uh you know section that used to just be a a part of
12:53
a of a SAS. Now everything's an agent. Um but it's too late now. We can't just
13:00
reel it in and say, "All right, guys. Let's let's slow it down. This is too much agent talk. Look, 95% of you are
13:07
kind of lying." Uh it's too late. Um, I was actually on the floor uh couple feet
13:13
away from Nvidia CEO Jensen Wong uh when he said, "The age of Agentic AI is
13:18
here." And normally uh when Jensen Wong makes a bold statement like that, it is the truth.
13:25
And you have to follow the biggest players because everyone is all in on
13:31
agents, right? Uh Microsoft with their uh co-pilot studio in autonomous agents.
13:38
uh Google every single day, even uh a couple hours ago, announced some new agentic capabilities uh in Project
13:45
Astra, in Google Live and other places. And Open AI even had a pretty popular
13:51
splash a few weeks ago when they announced their first commercially available agent. So the biggest players
13:58
whose technology that many of us use every single day have essentially said
14:03
our focus is agents and what we are building is agentic AI. So you have to
14:10
be able to follow the writing on the wall and see where the big players are
14:15
playing and everyone's playing in the agentic space. But this has led to what I would call a
14:22
definition crisis. Uh a recent Capgeemini study found that 93% of
14:30
business leaders saw agents as one of their most strategic business initiatives. Yet only 2% have scaled
14:39
them. And this is something I talk with business leaders a lot and I've been able to learn a lot. But it is a
14:46
definition crisis because there is no agreed upon definition of what an agent is. Everyone's just saying you need a
14:52
gentic AI. But there is a huge gap between what both enterprise companies
15:00
are being sold, what we all think we need, and then the tools that are actually at our disposal today.
15:09
And this lack of education, I think, has led to a catastrophic failure rate of AI
15:15
implementation. So, a study just from a few days ago from MIT showed that right
15:21
now about 90 a different 95% stat, but 95% of generative AI pilots at companies
15:28
are failing right now, right? Some of these some of these studies and stats,
15:33
it seems like they almost contradict each other. But this is my reality as well. When I uh talk with with uh
15:40
business leaders on the Everyday AI podcast or when I'm consulting companies, I see this all the time,
15:45
right? Companies are like, "Yeah, we just, you know, signed up for this uh Agentic AI or we're using this AI agent
15:51
because we know we need to. Our competitors are, but this is leading to so many pilots failing." And again,
15:58
according to this MIT report, 95%, which is a staggering amount.
16:04
So, if you're making decisions at your company on Aentic AI, how do you not
16:11
fall into that trap? How can you be the 5% or hopefully it's bigger than that if
16:17
we're looking at the same study next year? It starts with literacy. It starts
16:23
with understanding the basics. And that's what I'm going to break down for you now.
16:28
Uh, so here on my screen, I like to call this the hierarchy of intelligence. All
16:34
right? Because so many companies are rushing to just implement whatever they
16:40
see as the most cuttingedge AI and handing it over to dozens or hundreds or
16:46
sometimes tens of thousands of employees that may not even know how to make use of it. Um, so when I talk about a
16:54
definition crisis, this is real, right? So even when I'm going over uh kind of my hierarchy of intelligence and how I
17:02
would categorize thing based on thousands of hours of conversations uh with the world's leaders in agentic AI
17:10
these are always changing just like you could argue the definition of AGI uh is
17:16
changing the more we talk about it and the more we build you could say the same thing for uh just these classifications
17:22
of AI. All right. So, I want to talk a little bit about large language models, AI powered workflows, agentic models,
17:31
and AI agents. All right. So, we're trying to undo some of this agent washing because uh if you like so many
17:38
others right now are in the middle of making pretty big decisions for your
17:44
company in terms of your tech stack, in terms of where you're investing in AI. I think it's important to just cut the
17:50
marketing, cut the BS and talk about it at its fundamental level.
17:56
Let's start with large language models. All right, most of us know and uh for the most part understand what a large
18:03
language model is. Uh a lot of people say these are you know autocomplete uh
18:08
you know AI on steroids, right? uh but they are for the most part large language models at their c at their core
are stateless and reactive and essentially next token predictors. they're, you know, much more than that.
But if you really want to simplify it, that's what you look at a large language model as for the most part. Um, working
18:27
with text, they're brilliant, but they're passive conversationalists.
18:33
AI powered workflows are a little different. So AI powered workflows are
18:38
humanes automations with predefined paths. So yeah, so many of those uh
18:45
things that are being marketed to you right now and you see those you know screenshots of all these you know charts
18:51
and flows and everyone's like look at this agent that is an AI powered workflow designed by humans with
18:58
predefined paths uh and they obviously use large language models to go from
19:05
step uh step by step a lot of times there's conditional logic um but it's a
19:10
fixed process right you move on from one step to the next.
19:16
Agentic models. This is where it gets tricky, right? Uh a lot of today's top
19:22
uh models including uh the new GPT5 uh also Gemini 2.5 Pro, they are agentic
19:32
by nature, right? So if you go to gemini.google.com google.com or chatgpt.com. The same argument could be
made for uh Claude's 4 models, but I really like to focus on the um uh GPT5
and um Gemini 2.5 Pro. So, in aentic model, it kind of starts to blend
19:52
between what is an agent. In aentic model, you can give it a problem, you
19:57
can give it a destination, and it's going to decide how to get there. Okay?
20:03
It has many different tools that it can use at its own discretion and it will often go back a couple of steps in its
20:10
path and maybe start over or maybe it will start to uh go down a certain road and it'll say oh okay actually uh I
20:18
thought that I needed to search the web for this. I should actually be uh running some Python here locally in
20:24
order to figure this out. So, agentic models uh they can um access kind of a
20:29
large language model, right? They are a large language model at their core, but they also have a set of tools to use and
20:35
they on their own accord decide how, when, and how long to use those
20:40
different tools. And then last, AI agents. AI agents are
20:47
completely different. For the most part, AI agents are powered, well, hopefully they're powered by an agentic model. But
20:55
the diff the difference is staggering. Okay, this is a complete system. AI
21:01
agents perceive, decide, and autonomously adopt to achieve complex
21:06
goals, right? So, for the most part, AI agents, you like to think of them as a
21:12
human sitting in front of a computer because a true AI agent would have access to uh a desktop, although it's
21:19
virtual. Uh right, they'd have access to a virtual terminal so they can run code. They have access to a virtual browser.
21:26
So in theory, I like to think of it as someone like you and me who's sitting in
21:32
front of a computer and look at all the different capabilities that they have access to where even an agentic model,
21:38
they can't uh launch a browser per se and navigate around a website and that
21:44
is one of the biggest differentiators between agentic models and true AI
21:49
agents. Uh so you can see now hopefully by looking at the differences just the
21:54
amount of agent washing that is going on and how so few agents are actually
22:01
agents. Okay, so now you know some basic definitions
22:07
uh and where we're at currently with agentic AI. So I'm going to give you some best advice on where you should be
22:14
going. And again, I've stolen all this information from the smartest people in the world. All right. So, keep that in
22:19
mind. Uh, here's the problem. I think everyone's focusing when it
22:25
comes on to AI agents, one agent to rule them all, which I think is an absolute
22:32
recipe for disaster. Uh, that's why I even think OpenAI's,
22:37
you know, chatbt agent, it's okay, but it's not that great. It's a general use case agent. Uh in the same way you could
22:44
look at uh narrow intelligence versus general intelligence. Narrow
22:49
intelligence is always easier to solve. Uh it's always an easier goal to work toward. And I think this is um something
22:56
that we should be looking at. A lot of companies when they're looking at agentic AI or AI agents, they're really
23:02
looking at something that can um disrupt or replace an entire workflow or an
23:10
entire job description. And I don't think we're there yet. Um, yes, we can
23:16
build general agents that can do a fairly okay job, but if you want to talk about getting an ROI and being in that
23:24
5% of companies that are getting uh their agentic AI implementations correct, it's looking at narrow use
23:31
cases, very specific, and then finding a specific agent that does that specific
23:36
use case. Yet, everyone's just rushing when they're looking at an AI agent. like I want something that can automate
23:42
a toz and that's not the way that we should be looking at it.
23:47
So here's what I like to say the four pillars of a winning agentic strategy.
23:53
Uh, number one, you should be building on agent. You should be building on giants, right? I'm just being honest.
24:00
Um, there's so many promising uh AI agent startups that are really good and
24:06
a lot of times better than what we may be getting from some of those giants.
24:11
There's a risk involved, right? You shouldn't be moving your dayto-day core business operations around a startup. um
24:19
you know are like unless you're talking about Open AI Enthropic or a company
24:25
that has a trillion dollar market cap. If you're putting your day-to-day business operations uh on a startup
24:32
that's risky. Number two, like I said, narrow before broad. You should be
24:38
looking at small, short, measurable tasks that an agent can complete. And
24:44
make sure if you are uh diving into the agentic AI side again measure and use an
24:51
actual agent. Uh the the next one you know a lot of people are always like hey if we're using AI or AI agents and we
24:58
get these you know 30 40 50 or you know if you look at Mackenzie digital study you know up to 70% productivity savings
25:06
what should we be doing with all of this save time. Uh right I think you need to be focused on expert reasoning data. So
25:14
yesterday year's large language models have been powered uh by essentially all
25:19
the in all the internet right um where your company is really going to be able
25:25
to create separation especially when it comes to AI agents is capturing what's
25:31
in your key decision makers what's in their brains how do they um make
25:37
decisions how do we know when we look at all of this data how do we know what to be looking that I like to call that
25:43
expert reasoning data that is the fuel for future agents, right? We've talked
25:49
about rag for the last few years. Uh and that can, you know, hopefully give a
25:54
large language model better results. I like to think of expert reasoning data as that rag for agents, right? Uh I'm
26:01
not saying we've we we've hit a wall in terms of uh the amount of training data that large language models can ingest,
26:08
but we're getting close. And I think your company should be focusing on all of that unstructured decision-making
26:15
data that lives in your key employees heads. And then last but not least, agents in the browser. I think you
26:21
should focus on seamless implementations that work where your team works. Right? You need you need to make sure that
26:28
whatever AI agents you're using, they have access to the exact same tools and software that your leaders use.
26:36
All right. So, I gave you some best advice, but now let's quickly go over before we wrap the three deadly sins of
26:43
agentic AI. So, mistake number one is prioritizing
26:50
agentic tools over large language model education. When companies reach out to
26:55
me and ask about AI agents, I like to say, "What percentage of your staff that
27:02
sits in front of a computer can tell me what a large language model is?" Usually, if business leaders are being
27:10
uh truthful, it's a very low percentage. I think we're jumping forward, and it's
27:15
hard not to with the pace of AI innovation. And this is coming from someone that literally talks about this
27:22
for hours every single day. It's hard not to get caught up in this momentum,
27:28
in this hype, and in this fear of missing out. But I think that's one of our biggest issues right now. Uh you
27:36
need to stress the basics of Gen AI education. people need to understand
27:42
kind of in the same way that I leveled out the four different tiers of uh current AI uh knowledge. You have to
27:50
understand what a large language model is and how it works before you can move on to an AI powered workflow before you
27:56
can move on to an agentic model before you can move on to AI agents. So it's always a rush to keep up with what's
28:03
today and what's next. But you cannot skip over literacy, right? because then
28:08
we are asking people to do a new day-to-day um tasks without being able
28:14
to even understand the language. That's mistake number one.
28:20
Mistake number two is ignoring the onederee misalignment problem. And this
28:25
is about so much more than hallucinations. Right? Even though uh today's large language models,
28:31
specifically agentic models uh are doing much better when it comes to reducing their hallucination rate. The onederee
28:38
misalignment problem is when we talk about agentic AI specifically multi-agentic AI right now if you or I
28:46
are using an agentic model we hopefully as the human can spot if something is
28:52
wrong and course correct let's let's be honest here the future of
28:58
AI agents isn't just working with one agents uh it's agentic swarms it's multi-agentic orchestration and that is
29:06
a huge problem. Uh yes, it's uh a nice and fun and shiny thing to look at, but
29:12
that one degree off, if that compounds over multiple agents without the correct
29:18
human oversight, that 1% off is going to become a total miss because it is going
29:23
to compound silently across an agentic swarm.
29:29
And then the third one, and this one, this one grinds my gears. thinking that
29:35
human in the loop is some sort of AI strategy. It's not. Uh it is a passive
29:42
crutch. I like to talk about expertdriven loops. And that is if you
29:48
are already in the middle of your first uh kind of agentic AI or AI agent um
29:54
implementation at your company, you need to get out of this habit of AI in the loop. Right? A good example, I' I've had
30:01
many conversations like this. you know, companies are saying, "Oh, yeah, we have a human in the loop on on these agents
30:07
out there running." And usually it's someone in IT, right? And then it's like, "Okay, what does Bill from it know
30:14
about this financial forecasting agent that's sending information directly to prospective clients?" And it's like,
30:20
"Oh, well, yeah, he knows nothing." And then it's like, okay, how often is Bill revisiting this loop? Oh, well, only
30:26
when there's a problem. We need to shift that mindset. Human in the loop is passive. It is ancient. And I think it's
30:33
a crutch in another potential recipe for disaster. I think we need to shift toward and think about an expertdriven
30:41
loop that is where we are using this expert reasoning data and putting the
30:46
right people in the right agentic loops at the right time. Whereas in theory, Bill might be overseeing 20 agents that
30:55
he maybe doesn't know anything about. Sorry, Bill from it. Uh whereas if we really want an AI agent that works, we
31:02
might have 20 experts working on one agent. So we need to flip the script and
31:08
actively put expert decision-m into our AI flows. All right, I hope this was
31:14
helpful. I appreciate everyone's time on this. Uh you know, if you want to know more, uh you can go to our website at
31:21
your everyday.com. We do this podcast literally every single day, Monday through Friday. talk
31:26
to a lot of smart people about AI agents. And actually, we have uh for two hours if you go sign up for our
31:31
newsletter, there's a little surprise for you at the bottom uh of our autoresponder.
Agentic AI - PWC Booklet - Download Free
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AI Agents in Action
Micheal Lanham
Simon and Schuster, 25 Mar 2025 - Computers - 344 pages
Create LLM-powered autonomous agents and intelligent assistants tailored to your business and personal needs.
From script-free customer service chatbots to fully independent agents operating seamlessly in the background, AI-powered assistants represent a breakthrough in machine intelligence. In AI Agents in Action, you'll master a proven framework for developing practical agents that handle real-world business and personal tasks.
Author Micheal Lanham combines cutting-edge academic research with hands-on experience to help you:
• Understand and implement AI agent behavior patterns
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• Leverage the OpenAI Assistants API and complementary tools
• Implement robust knowledge management and memory systems
• Create self-improving agents with feedback loops
• Orchestrate collaborative multi-agent systems
• Enhance agents with speech and vision capabilities
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Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.
About the technology
Most production AI systems require many orchestrated interactions between the user, AI models, and a wide variety of data sources. AI agents capture and organize these interactions into autonomous components that can process information, make decisions, and learn from interactions behind the scenes. This book will show you how to create AI agents and connect them together into powerful multi-agent systems.
About the book
In AI Agents in Action, you’ll learn how to build production-ready assistants, multi-agent systems, and behavioral agents. You’ll master the essential parts of an agent, including retrieval-augmented knowledge and memory, while you create multi-agent applications that can use software tools, plan tasks autonomously, and learn from experience. As you explore the many interesting examples, you’ll work with state-of-the-art tools like OpenAI Assistants API, GPT Nexus, LangChain, Prompt Flow, AutoGen, and CrewAI.
What's inside
• Knowledge management and memory systems
• Feedback loops for continuous agent learning
• Collaborative multi-agent systems
• Speech and computer vision
About the reader
For intermediate Python programmers.
About the author
Micheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning’s Evolutionary Deep Learning.
Table of Contents
1 Introduction to agents and their world
2 Harnessing the power of large language models
3 Engaging GPT assistants
4 Exploring multi-agent systems
5 Empowering agents with actions
6 Building autonomous assistants
7 Assembling and using an agent platform
8 Understanding agent memory and knowledge
9 Mastering agent prompts with prompt flow
10 Agent reasoning and evaluation
11 Agent planning and feedback
A Accessing OpenAI large language models
B Python development environment
Preview
https://books.google.co.in/books/about/AI_Agents_in_Action.html?id=_-1JEQAAQBAJ
Maria Johnsen
Maria Johnsen, 2 Jan 2025 - Technology & Engineering - 514 pages
In Agentic AI, readers are taken on a compelling journey into the transformative world of autonomous artificial intelligence. This in-depth exploration covers the evolution of agentic systems, from their historical roots in early automata to their role in shaping future technologies. The book delves into the philosophy of agency in machines, examining the intricate balance between control and independence, and how these systems are redefining fields such as healthcare, defense, space exploration, and creative industries.
With chapters focused on the critical components of agentic AI including decision-making, learning, goal-orientation, and ethical considerations Maria Johnsen sheds light on both the technical and moral implications of creating AI systems capable of autonomous action. The book also addresses pressing concerns such as privacy, bias, fairness, and the societal impact of AI, offering insights into its integration into diverse sectors, from smart cities to autonomous transportation.
A particularly poignant section highlights the moral responsibility of agentic AI, exploring how ethical frameworks can guide the development of these systems and ensure accountability in their decision-making processes. The future of work and the potential for AI to disrupt industries and create new roles is also examined, with a focus on preparing society for the inevitable changes on the horizon.
Through case studies, expert insights, and future predictions, Agentic AI offers a comprehensive look at how autonomous systems are shaping our world and what lies ahead in the next frontier of artificial intelligence.
Preview
https://books.google.co.in/books/about/Agentic_AI.html?id=bMg7EQAAQBAJ&redir_esc=y
AI Agents: Building and Selling Your Digital Genius
DAVID. HOLMAN
Amazon Digital Services LLC - Kdp, 12 May 2025 - Computers - 494 pages
Transform Your Vision into a Cutting-Edge AI Reality
Imagine harnessing the power of artificial intelligence to create digital agents that think, learn, and perform autonomously. This comprehensive guide is your essential companion to mastering every step-from the foundational concepts to the intricate technical details of AI agent development. Whether you're an aspiring developer, entrepreneur, or innovator, you'll find expert insights and actionable strategies designed to turn ideas into impactful AI solutions.
Dive deep into the core principles behind AI agents and explore the building blocks that shape their intelligence. Gain clarity on critical topics such as machine learning basics, data collection, and preprocessing that lay the groundwork for successful AI projects. Navigate the complexities of programming, training, and optimizing AI agents with practical guidance on algorithms, architectures, and performance monitoring.
But it doesn't stop at development. This book also walks you through the practical steps of integrating AI agents seamlessly into your systems while addressing security and ethical considerations to build trust and reliability. Learn how to design intuitive user interfaces that enhance user experience and effectively test, deploy, and maintain your AI products.
Ready to break into the AI marketplace? Discover proven commercial strategies-from market analysis and branding to sales tactics and partnerships. Understand regulatory landscapes and explore ways to scale your AI business to new heights. Real-world case studies highlight successes and lessons from industry leaders, giving you valuable perspectives to fuel your journey.
With expert guidance tailored to both technologists and non-technologists alike, this book empowers you to confidently build, market, and sell your digital genius. Prepare to transform potential into performance and innovation into success.
