Speaker A
Right now, somewhere in the world, an AI agent is booking flights, writing code, and answering customer questions.
Explore why AI agents need specialized operating systems to manage tasks, memory, tools, security, and governance for reliable AI deployment.
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Key Takeaways
- AI agents require an operating system to coordinate and manage their actions effectively.
- Without an agent OS, AI agents behave unpredictably and pose operational risks.
- The agent OS provides memory, scheduling, security, and governance to make AI agents reliable.
- Implementing agent operating systems is essential for scaling AI in real-world applications.
- Teams adopting agent OS infrastructure will build more trustworthy and efficient AI systems.
What the video covers
- Current AI agents operate without memory or supervision, leading to inefficiency and risk.
- An operating system for AI agents acts like a principal managing a school, organizing tasks and resources.
- Traditional OS concepts like scheduling, memory management, and access control are adapted for AI agents.
- AI agents are digital workers performing tasks like booking flights, coding, and customer service.
- The agent OS has three layers: AI agents, the OS kernel (management), and infrastructure (hardware, models, tools).
- Key OS components include scheduler (orchestrator), memory manager, tool manager, identity manager, observability, and guardrails.
- Schedulers prioritize tasks to optimize resource use and urgency.
- Memory management enables agents to retain short-term, long-term, and episodic memories for context-aware actions.
- Tool management safely controls agent access to external systems via sandboxing.
- Governance and observability ensure security, auditability, and human oversight for critical decisions.
Chapters
- 00:00Introduction to AI Agents and Their Limitations
- 00:29What is an Operating System for AI Agents?
- 01:01Analogy: School Without a Principal vs. With a Principal
- 01:58Traditional Computer Operating Systems Explained
- 03:08The Rise of AI Agents as Digital Workers
- 03:47Problems with Current AI Agents' Lack of Supervision
- 04:40The Need for an Operating System for AI Agents
- 05:05Three-Layer Architecture of Agent Operating Systems
- 06:10Scheduler: Managing Task Priorities and Resources
- 07:18Memory Manager: Giving AI Agents Memory
Full Transcript — Download SRT & Markdown
Speaker A
And it has absolutely no idea what it did five minutes ago. It's like giving a genius goldfish the keys to your company.
Speaker A
Today, we're going to fix that. We're going to talk about something that sounds incredibly boring, but I promise you it's not.
Speaker A
We're talking about the operating system, but not the kind on your laptop. We're talking about the operating systems for AI agents.
Speaker A
And by the end of this video, you're going to understand why this might be the most important infrastructure nobody's talking about.
Speaker A
But first, let's start with the basics. Imagine you're 10 years old and your school has no principal.
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No one assigns classrooms. No one makes sure the buses run on time. No one stops kids from using the art supplies for a food fight.
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Would you say that's chaos? I know I would. Now, imagine the principal shows up.
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Here's our principal, and he's wearing a cowboy hat and boots because we are in Texas.
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Suddenly, there's a schedule. Math is in room 101, lunch is at noon, the bus is now nowhere to go, and if someone tries to set something on fire, there's an assistant to handle that too.
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That principal, that's an operating system. On your computer, the operating system is the invisible manager that makes everything work together.
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When you click on Spotify, the OS figures out how to get the sound to your speakers.
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When you open Chrome and Word at the same time, the OS makes sure they share the computer's brain without fighting.
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When you plug in a USB drive, the OS says, "Oh, hey, new thing," and makes it available.
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You never see it, you never even think about it, but without it... your computer is just an expensive paperweight.
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Windows, Mac OS, Mac OS, and Linux. These are all operating systems. They manage memory, schedule tasks, control who can access what, and keep everything from crashing into everything else.
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It's simple, right? Now here's where it gets interesting. We've entered the age of AI agents.
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These aren't just chatbots that answer questions. These are AI systems that can actually do things.
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So they're doers. They can book your flight, follow your expenses, write and run code, send emails on your behalf, talk to other agents, and get work done.
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They're like little digital employees. But here's the problem. Right now, most AI agents are running around like little toddlers.
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Here they are, little AI agent toddlers, and no one's supervising them. They forgot what they were doing.
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They don't know what tools they can have access to. They can't tell you what they did or why.
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They have no concept of, "You probably shouldn't delete that database." Every time you start a new conversation, it's like they've had their memory wiped.
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Hi, I'm your AI assistant. What's your name? Buddy, we've talked 14 times this week.
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And when you have multiple agents trying to work together, it's like putting five toddlers in charge of a restaurant.
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Someone's going to end up in the soup. And that is why we need this principal right here to supervise.
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What we really need is an operating system for AI agents. An agent operating system does for AI agents what Windows does for your applications.
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It manages resources, it schedules tasks, it handles memory, it controls access. It keeps things from going horribly wrong.
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Let me show you how it works. Think of it as a three-layer cake.
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At the very top, you have your AI agents. It wouldn't be a cake without candles.
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So let's add some candles. These are the workers: the travel agent, the coding agent, the customer service agent.
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They each have a job to do. In the middle, you have your agent OS kernel, the operating system kernel.
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There it is. This is the principal's office. Remember the cowboy we talked about? Cowboy principal back in the game.
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There we go. This is where all the management happens. And at the bottom, you have the infrastructure layer.
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This is where you have the actual computers, the AI models, the databases, the tools your agents use.
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Now, let's zoom into the middle layer because this is really where the magic happens.
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First up, we have the scheduler. And we also refer to the scheduler as the orchestrator.
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This is the principal's calendar. When 10 agents all want to use the AI brain at the same time, someone has to decide who goes first.
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Is the customer complaint more urgent than the weekly report? The scheduler figures that out.
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Let me give you a real example. You have a customer service agent handling a live chat and a background agent summarizing yesterday's tickets.
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The scheduler makes sure the live customer doesn't wait while the background task hogs all the resources.
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Next, we have the memory manager. Remember our goldfish problem? This fixes it. The memory manager gives agents the ability to remember.
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Short-term memory for the current conversation, long-term for the things that happened last week, even episodic memory, like remembering the last time I tried a certain approach, it failed.
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A real example would be your HR agent remembers that you asked about parental leave last month, so when you come back...
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It doesn't start from scratch. Next is the tool manager. Agents need to do things in the real world, right?
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Send emails, query databases, call APIs. The tool manager is like a carefully organized toolbox.
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It knows what tools exist, who's allowed to use them, and critically, it runs them in a sandbox.
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Why a sandbox? Because if an agent writes code and runs it, you really don't want it accidentally deleting your production database.
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So the sandbox is kind of like a padded room where the agent can try things without burning down the house.
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A real example of this would be a coding agent that can write and execute Python code, but it can only touch files in a specific folder.
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It can't see your passwords, it can't access the internet unless you say so. Next, we have the identity manager.
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This answers the question, who are you and what are you allowed to do? Just like you have a badge to get into your office building, agents need credentials too.
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These are short-lived tokens that expire, permissions that limit what they can access. A clear chain of this agent is acting on behalf of this user.
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A real example of this would be when your travel agent books a flight using your credit card, there's a clear audit trail of who authorized what.
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Next, we have my favorite, observability. It's the security camera system. Every decision the agent makes, every tool it calls, every response it generates, it's all logged and traceable.
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If something goes wrong, you can rewind the tape and see exactly what happened. Let me give you a real example of this.
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Your agent approved a refund and it shouldn't have. With observability, you could trace back through the entire decision chain and figure out why.
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And finally, we have guardrails. Governance. These are the rules, the boundaries, the "hey, maybe don't do that" system.
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Input guardrails check what's coming in. Is someone trying to trick the agent with a malicious prompt, for example?
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And output guardrails check what's going out of your agent. Is the agent about to say something inappropriate or incorrect?
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And governance is the policy layer. Some actions require human approval. We also call this the human in the loop.
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Some data is off limits. Some decisions are just too important to automate. A real example of this would be an agent that can process refunds under $50 automatically.
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Over $50, a human has to approve it. So why should you care about all of this?
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Well, because AI agents are not a future thing. They're now things. Teams are deploying agents that handle real customer interactions, real money, and real decisions.
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And most of them are doing it without this infrastructure. That's like running a city without traffic lights.
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It works until it really, really doesn't. Teams that implement agent operating systems first will be able to scale AI systems efficiently and...
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Everyone else will be stuck with expensive, fragile, goldfish brained experiments. So it's safe to say that without an agent operating system, teams risk deploying unreliable and inefficient AI agents.
Speaker A
So let's recap. An operating system manages resources so applications can work together without chaos.
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An agent operating does the same for AI agents, scheduling, memory, tools, identity, observability, and guardrails.
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Without it, agents are brilliant, but unreliable. With it, agents become infrastructure you can actually trust.
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The age of AI agents is here. The question is, who's going to be the principal?
Topics:AI agentsagent operating systemAI infrastructurememory managementtask schedulingtool sandboxingidentity managementobservabilitygovernanceIBM Technology
