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AI Agents in 2026: A Practical Blueprint for Competitive Value
AI agents promised radical change—but until 2026, most business leaders found them either overhyped or underdelivering. Recent advances from OpenAI, Google, Microsoft, Anthropic, and a surge of open-source projects like OpenClaw have pushed AI agent capabilities into tangible business operations. This article unpacks specific, actionable insights from the latest industry analysis on AI agents, focusing on terminology clarity, business integration, risk management, and real-world use cases.
AI Agent Definition: Beyond the Hype and "Agent Washing"
According to Gartner and other industry studies cited in this latest analysis, the term “AI agent” is heavily misused. Less than 5% of tech vendors marketing “agentic capabilities” actually deliver products with genuine autonomous functionality. Most so-called agents are simple chatbots or pre-scripted workflows with a thin layer of AI for basic decision support.
Real AI agents in 2026 are defined by:
Permissioned access to tools and systems
Goal orientation, not just query-answering
The capacity for autonomous decision-making, including retracing steps or changing approach if initial efforts fail
The ability to plan, execute, check results, self-correct, and even spawn sub-agents for complex, multi-system tasks
For example, these agents are increasingly common in enterprise platforms like ClickUp, Salesforce, Slack, and HubSpot—where traditional “features” now operate with agentic autonomy.
Current State: Enterprise Adoption and Market Penetration
Enterprise adoption of AI agents is no longer theoretical:
Over 80% of Fortune 500 companies report using active agents in production
40% of enterprise applications are expected to embed AI agents by the end of 2026 (per Gartner)
Thousands of business software tools now deploy agents for project management, sales automation, knowledge work, and customer support
This market uptake is compounded by the systemic integration of agent capabilities into mainstream AI models. Frontier models such as Google Gemini 3 Pro, Claude Opus 4.6, and OpenAI’s latest GPT suite now deliver agent-like reasoning and problem-solving out-of-the-box—capable of downloading resources, crafting custom code, and multi-stepping through complex business processes.
AI Agent Capabilities: From Autonomy to Risk
Modern AI agents act similarly to a new hire given unrestricted access to business systems. They route work, execute research tasks, build presentations, and even initiate commerce transactions—autonomously and at scale. Their growth is outpacing conventional control: for instance, these agents can independently download large datasets or code libraries, connect disparate SaaS tools, and execute workflows that span numerous platforms.
However, this autonomy increases risk. A study from Cybersecurity Insiders reports that 75% of Chief Security Officers have discovered unsanctioned AI tools (including agents) active within their stack. Moreover, 92% of organizations now lack comprehensive visibility into all AI “identities,” introducing significant security and compliance gaps. Without strong guardrails, agents can inadvertently bypass policy, generate compounding errors, or trigger unintended actions—especially when woven into multi-agent systems.
Business Decision-Making: When to Deploy AI Agents
A crucial insight for business leaders lies in the decision framework for agent deployment:
Static, well-defined processes: If a workflow is checklist-driven and rarely changes, AI-powered workflows or basic automation tools suffice.
Dynamic, variable processes: When steps change, require judgment, or span multiple business systems, agentic models or full agents provide measurable business value.
Broken internal processes: Agents should not be deployed onto flawed, undocumented, or outdated workflows; this typically accelerates negative outcomes.
Optimal agent ROI emerges from layering agents onto already efficient, well-documented, AI-ready processes—backed by robust standard operating procedures (SOPs) and clear ways to measure performance, error rates, and risk events.
Integration Phasing: Bounded Autonomy and Auditability
The integration of AI agents is most impactful—and least risky—when approached as an incremental release, not an all-or-nothing switch. A recommended process:
Suggest-Only Phase: Agents operate in draft mode. Outputs are generated, but human review and approval are mandatory before action.
Execute-with-Approval Phase: Agents can initiate tasks, subject to single-click human authorization.
Guardrail-Driven Autonomy: With performance and audit trails established, agents operate semi-autonomously within strict permission, spending, and process boundaries.
Key priorities in each phase: focus on clear measurement (time saved, error rates, risk mitigation), observability (full activity trails), and internal accountability (ongoing process audits).
Practical Use Cases: Immediate Entry Points for Value
Immediate business value from AI agents is already being realized in three low-risk use cases:
Meetings to Action Items: Agents draft owners and follow-ups after meetings, with human approval gating final distribution.
Inbox Triage: Agents prioritize, flag, and summarize high-importance messages, without enacting actions autonomously.
Research Brief Creation: Agents compile and document supporting evidence for reports, again subject to supervisor vetting before dissemination.
These use cases minimize systemic risk and build literacy in measuring agent-driven productivity gains.
Advanced Considerations: Agent Ecosystems, Not Lone Solutions
Businesses establishing “agent ecosystems”—networks of interoperable agents designed for process collaboration—are best positioned for scalable value. Short-term pilot projects should be sandboxed, measured, and backed by continuous improvement loops. Crucial to this effort is moving beyond superficial “human in the loop” models in favor of expert-driven, cyclical audit and evaluation processes.
AI Agents in 2026: Key Takeaways for Value Creation
Clarity and Precision: Discern the difference between real agentic AI and simple automation to avoid vendor “agent washing.”
Controlled Integration: Layer agents onto robust processes and audit every new deployment for traceability, observability, and compliance.
Incremental Autonomy: Employ bounded autonomy and maintain full activity trails before granting increased agent permissions.
Organizational Literacy: Start with low-risk, measurable pilots to build in-house capability with agent governance and evaluation.
Long-Term Ecosystems: Invest in agent ecosystems for future resilience, but never without clear standards for risk management and returns analysis.
The business advantage of 2026 will not be awarded to those with the most agents, but to those who integrate them most intelligently, with control, measurement, and continual process redesign driving real, observable value.
Topics Covered in This Episode:
- AI Agents 2026: Definition & Overview
- AI Agents vs Traditional AI Chatbots
- Current State of AI Agents in Enterprises
- AI Agent Adoption Statistics & Market Trends
- Key Differences: Workflow Automation vs AI Agents
- Types of AI Agents: Task, Process, Decision
- Autonomous vs Passive AI Agents Explained
- Real-World Risks: Security & Shadow AI Tools
- AI Agent Use Case Decision Framework
- Best Practices: Starting AI Agent Pilots
- Measuring AI Agent ROI & Performance
- Building Effective Agent Ecosystems
Episode Transcript
Jordan Wilson [00:00:16]:
Whether you're newer to AI or if you consider yourself an AI expert, we're all probably thinking the same thing. AI agents, AI agents, AI agents. That's because, well, we've been promised that AI agents would take over maybe in a good way, maybe in a bad way yet that promise never really came to fruition. I mean, we heard it in the early chat GPT days, 2023, 2024. 2025 was supposed to be the year of the agents, but it wasn't. But guess what? We're there. I think we've actually arrived with some of the recent AI updates from the big companies in in anthropic, OpenAI, Google, Microsoft, even crazy viral open source projects like OpenClaw. I think now the long vision that we've been pitched of AI agents is here, but it's all happening at once.
Jordan Wilson [00:01:17]:
Like I said, I've been doing this everyday AI thing for three years. And at least when it comes to AI agent development, the last month has been as eventful as the other two point nine years combined. Don't worry, even if you are brand new or if you're an expert. That's what we're gonna tackle on today's show. We're gonna hopefully get you caught up, tackle AI agents from all angles and do so in a hopefully quick beginner friendly way. So this is the start here series, and we're gonna be going over AI agents in 2026, what they are and when you should use them. Alright. I'm excited for this one.
Jordan Wilson [00:02:03]:
I hope you are too. What's going on? Welcome to the start here series. My name is Jordan Wilson, and the start here series, well, I created it because after 700 plus episodes of the everyday AI podcast, I didn't have a good answer for the most common question I got. People say, hey, Jordan. Looks like a lot of great information here on your podcast. Where do I start? Well, you start here with the start here series, and this is volume eight in the start here series. But this is an essential podcast series to both learn the AI basics and to double down on your AI knowledge. And the way that you should really do that is make sure you go to starthereseries.com.
Jordan Wilson [00:02:43]:
That is going to give you free access to our inner circle community in our start here series space. So you can go catch up on all of everything in this series in one place, a bunch of additional resource resources that we've thrown together, all available for you right there. So, like I said, whether you're brand new, an expert, it doesn't matter. Go join now, like, a thousand people that are already in the community, and listen to all of the start here series. Also, I have to put this out there because it's important. Make sure you also go listen to episodes seven twelve and seven thirteen. That is our twenty twenty six AI predictions and road map series. You know, 26 kind of bold predictions on AI, and at least a third of them are about agents.
Jordan Wilson [00:03:29]:
Alright. So, and if you missed our last start here series, that was volume seven where we went over context engineering, how to get expert level outputs from AI chatbots. But let's get straight into AI agents, kind of the state of AI agents here in 2026. So here's what we're gonna be going over on today's show. We're gonna talk about what AI agents actually are and how they're different from, how they're different from traditional AI chatbots or AI powered automation. I'm gonna give you a decision framework for when agents are the answer and when they're worth it versus when they're just overkill and when you should say, hey. That should just be a human task or an AI, you know, large language model task. And then I'm gonna give you the practical path to safely starting your first agent pilot, not even this year, like this week.
Jordan Wilson [00:04:19]:
Alright. Let's get the explanations out of the way. All right. So like I said, if you are, if you've been building AI agents for, you know, a decade or two, you can just skip ahead a couple of minutes. Actually, speaking of that and why I think it's so important to look at this from a foundational level. So, about two weeks ago, had a fantastic guest, on my podcast. She's the head of, Microsoft Research. She's been working in AI agents for twenty years.
Jordan Wilson [00:04:47]:
And one thing we talked about both on the show and before and after is the definition of an AI agent is constantly changing. Right? So even what is an agent and what's not an agent, what can an agent do, what can't it do, it's always changing. Alright. So if you're listening to this in, you know, February 2026, that's great. All this information is probably gonna be extremely fresh and accurate. If you're listening to this in June or in 2027, right, Some of this might be a little old, so keep that in mind. But I'm gonna give you at least as of February, you know, nineteenth twenty twenty six. This is the realest most up to date.
Jordan Wilson [00:05:29]:
So what is an AI agent? In very simple terms, it's kinda like an AI chatbot, but it has tools, and it has permissions, and it has a goal, and it makes its own decisions. And sometimes it might start off on one path and say this isn't right, and it'll go backwards and start down another path. So they can plan ahead, take steps, use tools, take actions, check results, and and retry within the guardrails, whatever those guardrails are, whether you set them up or they're set up by a certain, you you know, piece of software that you're using. So what do agents do? Well, they can take on delegate work. They can delegate work to other sub agents. They they don't just answer questions. Right? So they can draft route, research, navigate apps, and even compile multi step tasks across different systems. And here's the state of AI agents in in the enterprise.
Jordan Wilson [00:06:18]:
Right now, Gartner study show that 80% of fortune five hundreds have active agents, right now. Sorry. That was from, Microsoft. But, Gartner said that 40% of enterprise apps are projected to include them by 2026. So yeah. Even since that story or sorry. That, report came out from Gartner last summer, I think that has changed. Right? That's one of the things that's difficult to give, you know, people a a a thumbprint on kind of the state of AI agents is because even the studies that are the most up to date are usually looking in the rear view mirror a couple of quarters or more.
Jordan Wilson [00:06:55]:
Right? But the reality is a overwhelming majority of Fortune 500 companies have agents in production. Alright. And all the software that you use anyways. Right? Because you might think, oh, you know, an agent means building something on your own. No. It doesn't. Right? And it's not just the big four, in Microsoft, Google, OpenAI OpenAI, and Anthropic, right, that would give you kind of agents out there in the workforce. There's literally thousands of different now pieces of software, and that's where this, you know, Gartner stat comes from that 40% of enterprise apps are projected to include AI agents this year.
Jordan Wilson [00:07:31]:
Right? What that means? Good example I've I've talked about. I've used, you you know, ClickUp, a project management tool, in the past. Right? So now, like, ClickUp has, you know, agents by default. I think, you know, things that used to be considered just features or tools in daily software are now agents. Right? So if you're using, you know, Salesforce, they have agents. If you're using, you know, Slack, Slackbot, you know, they have agents. If you're using HubSpot, they have agents. Right? So, it's not just, you know, building an agent from scratch or, you know, using one of the tools for the big four or these, you know, super general agents like, you know, meta, meta's Manus agent.
Jordan Wilson [00:08:10]:
That's weird to say now. Right? Manus that was acquired by Meta or GenSpark or some of these, you know, general agents. Right? They're everywhere. So what is it actually? Right? So I'm gonna break it down in the most simplest, hopefully, language possible. An AI agent is an autonomous system that can plan, execute, and self correct across different tools and systems. So it's kinda like a human at a computer. Right? So that can make decisions and has access to things. That's the way I like to think of an agent.
Jordan Wilson [00:08:41]:
Right? It's the same thing as if a, you know, an entry level worker sat down at a computer at your company that had access to everything. Right? So think of that, but you can have million of them. So what feelings do you get? Is that excitement, or are you thinking of security and and risk and things that can go wrong? Well, I think when you think about agents and, their implications, you have to think of both sides of that coin. But that's essentially what an agent is. Right? A full agent is like sitting a human down at a computer that has access to everything. Right? It can go get you new clients and close deals, can complete work autonomously, spreadsheets, PowerPoints, right jumping between different apps. It, you know, has access to a terminal, you you know, a sandbox, a virtual browser, all these different things like a human sitting down in front of a computer, does. So, essentially, if it can't take action, if it can't adapt to context, if it can't solve new problems independently, it's not technically an agent.
Jordan Wilson [00:09:47]:
And I'm gonna go over some of the, you know, other things that it might be. And that's because I've talked about this on the show before. A great a great study from Gartner, talked about this concept of agent washing. Right? So they looked at, thousands of different vendors, in 2025 that were essentially marketing or offering or selling agentic capabilities. And what they found only a 130 of thousands were actually agents. It's agent washing. Right? Most are just using, you know, chatbots or, you know, scripted, rigid workflows and sprinkling some, you know, quote unquote agentic model somewhere in the process and saying it's an agent. I tell you this, even looking at my own inbox, right, for everyday AI, because, we get pitched, more than a thousand times a year by companies.
Jordan Wilson [00:10:41]:
And, you know, obviously, recently, it's just been all agents. Right? Oh, check out our agent. It's the best one in the world. Right? And most of the time, I don't look because it's I don't think most of the companies are gonna make it very long. But then when I do look, I'm like, this isn't an agent. Right? This is a, you know, like a like a workflow. This is, RPA with a a little bit of a large language model sprinkled on top. That's not an agent.
Jordan Wilson [00:11:04]:
Right? An agent, you don't give it a a dedicated workflow. You don't give it, you know, if then or if else, like like statements to to to follow. An agent is sitting a computer, like, sitting a human down in front of a computer. They have free will, make decisions, make good ones, make bad ones. It really depends on, well, the guardrails, that you set up around those agents. And right now, 88% of executives are investing in agentic AI, but most can't even tell what's real from fake. So let me quickly talk about the difference. Right? Because I said, chances are even if you're paying for an AI agent product, it's just marketing.
Jordan Wilson [00:11:47]:
It's smoke and mirrors. Because like I said, according to that Gartner study, less than 5% are actually AI agents. So let's first talk about AI powered workflows. Right? And there's nothing wrong with these other things, FYI. Right? But a great example of that is like n eight n, right, or or or make.com or, you know, in some instances, like Zapier as well. Right? I love I I love these tools. I love these platforms. Right? I'm not saying that that they're bad.
Jordan Wilson [00:12:13]:
Right. But if you have all these decision trees and you're connecting these nodes, it's not an agent. Right? It's an AI powered workflow. Nothing wrong with it. Alright? We're just getting definitions here. Alright. Then you have agentic models. Well, what the heck is that? Well, these agentic models are essentially turning into many agents, and they're technically, I think, in my opinion, more agentic than AI powered workflows, because they can make more free will decisions on their own and also in the same time, do more harm.
Jordan Wilson [00:12:44]:
Right? But agentic model, well, that's just our, you know, frontier AI models. Right? Think of the big four models. Well, technically, the big three, that are at least creating and, you know, putting their models out there. You could throw in, you know, XAI's, Grok if you want to. I wouldn't necessarily. But let's look at three models. Google Gemini three Pro, Claude Opus 4.6, and, OpenAI's GPT five two Pro. Right? Or, five three codex, whatever you want.
Jordan Wilson [00:13:17]:
And, obviously, if you're listening to this, you know, at some point later in the year, it you know, just replace it with, you know, today's three models. These models by default are agentic, and they didn't used to be. Right? A year ish ago, models were not agentic. They were, extremely smart auto complete token munchers. Right? Romp Romp Romp Romp
Jordan Wilson [00:13:39]:
Romp Romp Romp Romp Romp R Models are agentic by default. That means you can give them a lot of context. You can give them files. You can give them problems. You can even in the course of a chatbot. Right? A front end chatbot. You can technically if you know what you're doing and give it enough context and, you know, go back and forth with it, it can technically have a very agent esque workflow. Right? It's a little more confined because you're working, within the constraints of a certain, you know, AI chatbot like chat g b t.
Jordan Wilson [00:14:10]:
But I'm talking front end front end AI models technically are very agentic because they can decide on their own how they're going to solve a problem. They have tools that you probably don't even know that they have that they will call on their own. Right? They have virtual browsers. You you know, they have kind of their own, sandboxes where they can write and run code. Right? Crazy thing I was doing, recently, you know, using codex. Right. Very good. But, I mean, technically, I think opening eyes investing more and more in codex.
Jordan Wilson [00:14:45]:
You know, it's technically a coding model, but it's a front end model. Right? You don't have to do anything on the back end. I was using it, and codecs on its own decided it was going to download a six gigabyte open source model, and it used it in its own platform. That is crazy. Right? It noticed that it didn't have the capabilities to do something at scale that I asked it to. And then over the course overnight, it worked for almost ten hours. It downloaded an open source model. And then, essentially, I had, this one was, had, like, 3,000 screenshots, and it wasn't able to, you know, get the results it needed to at scale, at speed with its own computer vision model.
Jordan Wilson [00:15:27]:
So it said, alright. I'm gonna download something. And, a six gigabyte model, and it ran it through there. Right? That's a Magento model. It made decisions. It called tools. It found solutions. Right? Then you have passive agents.
Jordan Wilson [00:15:41]:
Alright. Sorry. I went on a little, side tangent there. So number one, AI powered workflows. Number two, agentic models. And then number three, passive agents. So these are agents that are not necessarily, autonomous. Right? But same thing.
Jordan Wilson [00:15:54]:
An example of that. Right? Chat g b t has a great agent mode. I can schedule that agent to run at 9AM. Right? But I still have to trigger it. I still have to go say, hey, agent. Right? And I can train it and give it, you know, its its role and its its its context and all this stuff, but I still have to go push the button. Alright? And then you have autonomous agents who are always on agents. Right? And then these are taking, especially recently, a a lot of new, shape and form.
Jordan Wilson [00:16:24]:
But, I mean, in always on agent or an autonomous agent is essentially that. It's working around the clock. Right? And it's not just triggered by you. It can, in theory, be triggered by a certain action like, oh, when you, receive an email. Right? It's going to automatically do a, b, and c, and then go after a certain goal. Right? So even the the the lines, you know, kind of going over, these four different, classifications, they're starting to blur as well. And the risks, just like the capabilities, are growing just as fast. Right? Some some, good stats here.
Jordan Wilson [00:17:00]:
So, this is from cybersecurity insiders, Said that seventy five percent of CSOs have discovered unsanctioned AI tools already running in their environments. Right? So that's that's crazy. Right? When we talk about shadow, shadow AI, it's no longer just, oh, someone's using, you know, chat g p t maybe when they shouldn't. No. What about if someone's building agents that have access to all of your data? It's happening. And then those agents are making decisions potentially without any or, you know, company approved guardrails. And and then you when you talk about multi agent systems, that's when there's huge new risks, right, that you it's it's really hard to project because as agents' capabilities are changing, humans necessarily, even the humans building them, don't fully understand the risk profile of these multi agent systems. Right? So a very simple example.
Jordan Wilson [00:18:00]:
Right? I didn't know that codex was gonna download an open source model. So what happens, right, when we set up guardrails, you know, on maybe a certain multi agent system, and then the agent gets creative. It finds loopholes or gray area. Right? And it says, hey. Well, it said I can't set up you you you know, my guardrails say that I can't use other agents, but it doesn't say I can't use, you know, AI powered workflows. So then it it finds a loophole, and now all of a sudden you have these agents that are setting up something that is, you you know, unregulated. Agents will do that. Agents, by default, are made to be, quote, unquote, helpful assistance just like large language models.
Jordan Wilson [00:18:43]:
So if they think that it's helpful, they're not going to, you you know, go through a a morality check first, or a a company ethics check unless you really have that hard baked in there. They're gonna go off and make trouble, probably. They're gonna break things while getting something done. You know, sometimes I think of agents, it's it's it's it's like giving a giving a toddler, a task. Right? Hey. Hey, child. Hey, three year old. You know, go go put this toy away.
Jordan Wilson [00:19:13]:
Alright. Well, did they put the toy away? Sure. Did they knock over a lamp? Maybe. Do they smear marker, on the new couch? Potentially. Right? It's the same thing. You know? And right now, another, report, the 2026 CISO AI risk report said that 92% of organizations right now lack full visibility into their AI identities. And again, as those AI identities and capabilities are growing, the risk is going crazy. So how do we get here? Right.
Jordan Wilson [00:19:47]:
Let's take a very quick walk. So 2022, you you know, AI was, you know, hey. Type a question, get an answer. And then I think we had two years, you know, in 2023 and 2024 where, you know, large language models, oh, they can use tools now, and you can upload files, and it can, you know, browse the web. And that's cool, and it's more helpful. But then I think toward the end of 2025 and, obviously, this year, now this is where these reasoning models are agentic by default, and their capabilities are growing by the day. And like I said, the flip side, the unknown risks are also growing. But now reasoning models and and reinforcement, learning just let agents actually act.
Jordan Wilson [00:20:27]:
Right? And even if we talk about things like OpenClaw, right, which which OpenAI, acquired, you you know, these open source agents, people are just giving them computers. They're handing over, you know, their complete lives. Right? Hopefully, maybe those are more entrepreneurs, business owners, solopreneurs. Right? But people are still doing this. They're giving AI agents access to everything, and we don't yet know their capabilities. But it's quickly gone from AI. It's just a chatbot that you talk with to, oh, now all of a sudden that chatbot has access to tools in the Internet. And, oh, now all of a sudden, you know, these agents are making decisions completely on their own, and their capabilities, are quick.
Jordan Wilson [00:21:08]:
So here's six different agent types that I think you'll encounter in 2026. You know, not every single agent out there. Like I said, there's literally tens of thousands technically agents because you can spin one up, in a couple of minutes. Right. But you have task agents. These are agents that draft, summarize, create assets, etcetera. You have decision supported agents. These are ones that compare options, surface trade offs, and flag risks for review.
Jordan Wilson [00:21:33]:
Then you have process agents. These are ones that route work across tools, gather inputs, and prep the next step. You have computer use agents. These can navigate websites and apps to complete multi step tasks. You have multi agent systems, right, when agents can spin up sub agents, you know, set a bunch of agents out on their own. Right? Claude Code, you know, from Anthropic recently, with their Opus 4.6, kind of, you know, have popularized that on the consumer and the mainstream. Then you also have commerce agents. Right? Where agents can talk to each other.
Jordan Wilson [00:22:04]:
Right? You know, agents are now having their own version of the web. Right? There's gonna be, agentic transactions, that really don't involve humans at all. So even the type of agents changing quickly. Right? Talk to me next week. There's probably gonna be new categories. So when do you actually need one? When do you need an agent versus when should I go do this task myself versus when do I need an AI powered workflow versus when should I just use chat g p t or Gemini or Claude or Copilot versus a full autonomous agent? Well, I think if you can write a clear checklist and it rarely changes, a workflow can handle it fine. Right? AI powered workflow. If the steps vary, if it requires judgment, or if it depends on multiple tools, that's probably when you're starting to get into either agent territory or an agentic model that has access to your data.
Jordan Wilson [00:22:53]:
But here's the thing. If the underlying process is broken before the AI touches it, an agent is not going to be the answer. I think a lot of people well, by a lot of people, I say the overwhelming majority of people are just looking at an AI agent as a shortcut to do things that humans couldn't or to do things that they just don't want to do. It's not gonna work like that. If you stick an agent on a broken or an antiquated workflow, you're just asking for a compounding disaster. Right? If you're like, oh, well, our humans aren't doing a good job at this. Let's give it to an agent. Well, the agent is going to do a worse job than the humans that weren't doing a good job.
Jordan Wilson [00:23:30]:
So you're really only being applying an AI agent to processes that are already running very smoothly, that are well documented. Right? You have SOPs. You have way to measure you have ways to measure them. Otherwise, setting an agent out is just a recipe for disaster. And that's why another recent Gartner study said that 40% of agentic AI projects are expected to fail or be canceled by 2027. Well, that's one of the big reasons is what I just told you. People are thinking of AI agents as duct tapes, instead of, you know, being multipliers, through processes that are already AI enabled and working with humans. Right? Poor data quality, just like anything else.
Jordan Wilson [00:24:10]:
Missing context. No evaluation loops, because I think success requires rethinking and redesigning processes, not just adding AI on top. Probably tired me saying, you know, you don't just upskill, reskill. Right? The same thing with agents. You don't just upskill a workflow with an agent. You don't just upgrade a workflow with an agent. You have to deconstruct it, rebuild. You have to unlearn, and you build from scratch an agentic first workflow.
Jordan Wilson [00:24:37]:
So we'll talk about bounded autonomy. And that's well, it's how you start losing control if you're not doing this. So you should be starting when it comes to agents. Like I said, pick the right workflow that's already working AI enabled with humans. It's documented. It's processed. It it it's processing correctly. You can measure it, etcetera.
Jordan Wilson [00:24:55]:
So you should start at suggest only with an agent where the agent just drafts and you decide what happens next. Once it's gone through that process successfully, then you move on to execute with approval where the agent acts after you one click sign off. Then, again, then and this is a big asterisk. Great. Everyone's gotta sign off. That's when you can start moving on to more autonomy. Right? When you can scale if the guardrails are right, you know, with spending caps, permission rules, and audit trails. Alright? But only then.
Jordan Wilson [00:25:27]:
Right? We can can you think of AI agents as this, you know, infinitely infinitely, scalable, asset for your company? So here's how you get started this week. Number one, you need to think about meetings to action items, have an agent go draft owners and follow ups, and you approve before sending, Inbox triage. Right? Another great thing for AI agents. Don't have them act. Right? Have it triage your inbox or multiple pieces of software that you already use, or creating research briefs. An agent can build a briefing doc with sources and let you verify them before sharing. So start very simple. Don't let an agent do something that right now requires multiple humans and multiple approval approval processes.
Jordan Wilson [00:26:11]:
Start looking at maybe some of those time consuming difficult tasks that an individual human does, and then don't give the AI agent full autonomy. Go through steps. So here is the big takeaway as we wrap up. Agents offer delegation, not just answers. They plan, act, and report back. They can use tools at their own disposal, come up with new solutions that you didn't even think of. So that's why guardrails, traceability, and observability are paramount. Then you need to start with bounded autonomy and measure time saved error rates, risk events, you you know, ROI.
Jordan Wilson [00:26:50]:
You need to constantly doing those things. You need to don't do human in the loop. Right? It's one of the biggest mistakes I think companies are still making because human in the loop was this fun term in 2024, and everyone just said it. No. Human in the loop is combustible in a bad way. Right? People think, oh, we're gonna have a 100 agents and we're gonna have, you know, Bill and IT oversee them. No. You need expert driven loops with cyclical improvement chains that are chains that are documented.
Jordan Wilson [00:27:20]:
Right? Observability traceability is a full time job for a team. Go back and that's why I said go back and listen to the 2026 AI prediction and roadmap series. I talked about this in a lot more depth and told you how to get it done. And then last but not least, the future advantage comes from building agent ecosystems, not just picking the right agent or, you know, dipping your toe in the AI agent of the week. You have to be intentional about it. And investments now, even sandboxing them not in production, investments now in proper AI agent usage are going to be key for when the technology improves. I know it's a tired old cliche, but AI agents are the worst are worse today than they'll ever be. They're only gonna get better.
Jordan Wilson [00:28:09]:
Alright. I hope this was helpful. A beginner's look at agents. So whether you are brand new to AI and you're getting all excited about all this, all these shiny capabilities or if you've been building AI agents for a decade, I hope that this episode and also the start here series has been helpful. And if it is, please please please go to starthereseries..com. That is going to give you insider access and free access to our inner circle community, and it's gonna spit you right out once you join, in the start here series, space there. So you can go, watch, listen, read more about all of the start here series in order connect with other people who are doing the same. Alright.
Jordan Wilson [00:28:54]:
I hope this is helpful. Thank you for tuning in. Hope to see you back tomorrow in everyday for more everyday AI. Thanks, y'all.