The Rise of Agentic Operating Systems
Goran Maurac
Helping SaaS & Fintechs with SEM, Conversion Optimization, Experimentation since 2001. @viaresponsa.com
The Rise of Agentic Operating Systems: Reimagining Computing for the AI Era
We stand at the precipice of a fundamental shift in how we interact with computers. The traditional paradigm of file-based operating systems designed for mouse-and-keyboard interaction is giving way to something far more revolutionary: the emergence of agentic operating systems. This transformation represents not just an incremental improvement in computing, but a complete reimagining of what an operating system can be in an AI-driven world.
The Current Computing Paradigm: Built for a Different Era
Today's operating systems—Windows, macOS, Linux—were architected decades ago around a simple premise: humans would directly manipulate files and applications through explicit commands. These systems "were built for a software architecture that is file-based and designed to be used with a computer and a mouse." This model has served us well, but it's increasingly misaligned with how we want to work in an AI-enabled future.
The limitations of this approach become apparent when we consider the complexity of modern digital tasks. We juggle multiple applications, switch between contexts constantly, and spend significant time on routine operations that could be automated. The traditional OS treats each application as an isolated silo, requiring human intervention to coordinate between them.
Enter the Agentic OS: A New Computing Philosophy
An agentic operating system represents a fundamental architectural shift. Instead of being built around files and applications, an OS built around AI could "look at defining computing as data-based, with intelligence" at its core. This new paradigm transforms the operating system from a passive resource manager into an active, intelligent orchestrator of digital tasks.
We're already seeing this transformation in action. Google's recent announcement of "agentic checkout" demonstrates how AI agents are beginning to operate autonomously within traditional computing environments, "letting users complete purchases directly within search results" and even enabling agents to "pay autonomously - removing humans entirely from the payment flow." This represents a fundamental shift from human-driven interactions to agent-mediated transactions.
AIOS, the AI Agent Operating System, exemplifies this approach by embedding "large language model (LLM) into the operating system and facilitating the development and deployment of LLM-based AI Agents." This integration addresses fundamental challenges like scheduling, context switching, memory management, and storage management—but with AI agents as first-class citizens rather than afterthoughts.
The core distinction lies in agency itself. While "AI systems are primarily copilots assisting humans in their work," an AI agent represents "a true autopilot, removing humans from the loop on a given task." An agentic OS extends this concept to the system level, where the operating system itself becomes an autonomous actor capable of managing resources, coordinating tasks, and making decisions on behalf of users.
The Building Blocks Are Already Here
The infrastructure for agentic operating systems is rapidly materializing. PwC's agent OS demonstrates "a consistent, scalable framework for building, orchestrating, and integrating AI agents across a wide range of platforms, tools and business functions." This enterprise-focused approach shows how organizations are already moving beyond AI experimentation toward systematic AI adoption.
Meanwhile, projects like Agent-S showcase the practical implementation of these concepts, enabling "autonomous interaction with computers through Agent-Computer Interface" and building "intelligent GUI agents that can learn from past experiences and perform complex tasks autonomously."
The technical foundation is also advancing rapidly. Innovative approaches treat "LLM agents as operating systems to manage memory, autonomously optimizing context use," solving one of the critical challenges in AI system design—efficient memory and context management.
2025: The Inflection Point
All indicators suggest that 2025 represents a critical inflection point for agentic AI. Industry analysts identify agentic AI as sitting "atop the list" of AI trends for 2025, representing "a new category of generative AI that operates virtually autonomously."
The evidence is everywhere. Google is currently running what may be "the most important A/B test on the planet right now" with their "AI Mode" - transforming their core search experience from "a wall of blue links" to AI-first results that "look much more like the latest from OpenAI or Perplexity." This test will determine whether Google cannibalizes their traditional search model in favor of an agentic approach, with "the results of this test" potentially meaning "the creation or destruction of trillions of dollars in market cap over the next 5 years."
Google's broader strategy reveals the scope of this transformation. They're evolving "search into a big AI chatbot" with integrated shopping features, virtual try-ons, price tracking, agentic checkout capabilities, and seamless integration with Gmail, Docs, and Calendar. This isn't just feature enhancement—it's architectural transformation toward an agent-mediated computing experience.
Major technology companies are already adapting, with Google announcing "Android XR, a new AI-powered operating system for augmenting our world with seamless context-aware content." This signals that tech giants recognize the need for AI-native operating systems designed around agent interactions rather than traditional user interfaces.
The Architectural Revolution
The shift to agentic operating systems requires rethinking core OS concepts. Traditional scheduling algorithms optimized for human-initiated processes must evolve to handle AI agents that operate on different timescales and with different resource requirements. Memory management becomes more complex when dealing with large language models that need to maintain context across multiple concurrent tasks.
Safety-First Architecture Design
Critical to this architectural evolution is the recognition that safety cannot be an afterthought. As Kozyrkov emphasizes, "This is why having a policy layer—a separate control system that governs AI behavior in real-time—is so critical." Agentic operating systems must be architected from the ground up with policy layers that can monitor, constrain, and redirect agent behavior without compromising system performance.
The challenge is particularly acute because agentic OS environments will involve multiple AI agents with varying capabilities and objectives operating simultaneously. Unlike traditional applications that run in isolation, these agents will need to coordinate, share resources, and potentially hand off tasks to each other—all while maintaining safety boundaries and audit trails.
Modern agentic frameworks already demonstrate sophisticated "monitoring and debugging tools" that "enable AI agents to continuously track and enhance their performance, helping to avoid operational errors and eliminate time-consuming automation bottlenecks." These capabilities hint at operating systems that are self-monitoring and self-optimizing, but they must be coupled with robust policy enforcement mechanisms.
The user interface paradigm also transforms fundamentally. Instead of clicking through menus and managing windows, users interact with their agentic OS through natural language goals and objectives. Users simply "define the goal" while the "Agent OS coordinates the resources needed to accomplish it across complex, multi-step workflows." However, this simplicity for users requires unprecedented complexity in the underlying safety and control systems.
Implications and Challenges
The transition to agentic operating systems carries profound implications. Organizations will need to rethink their IT infrastructure, security models, and user training approaches. This transition "raises important questions about data privacy" and requires careful consideration of ethical implications.
The Safety Imperative: Beyond Capability to Control
A critical challenge in developing agentic operating systems lies in understanding that "AI capability is not AI safety." As AI safety expert Cassie Kozyrkov warns, "Many executives conflate the two—assuming that if a system is 'smart,' it must also be 'safe.' They underestimate how brittle AI systems can be under real-world conditions and overestimate the power of pre-launch testing to catch every possible issue."
This brittleness becomes exponentially more dangerous when AI systems operate at the operating system level with broad access to system resources and user data. "AI risks emerge after deployment, in ways that no amount of foresight or testing can fully anticipate," making robust safety mechanisms essential for agentic OS architectures.
Recent chatbot failures illustrate these risks at a smaller scale. Air Canada's chatbot fabricated a bereavement fare policy and was legally required to honor it, while DPD's chatbot called its own company "the worst delivery firm in the world." These incidents demonstrate that "AI systems don't think for themselves" and "just turn the patterns you show them into recipes for creating more of the same."
The Policy Layer Solution
The solution lies in architectural design that separates capability from control. Kozyrkov advocates for policy layers—"a separate layer of logic that sits on top of the AI system to control behavior and enforce rules." In an agentic OS context, these policy layers become even more critical, serving as "the AI equivalent to human etiquette" but operating at system-wide scale.
For agentic operating systems, policy layers must address not just content filtering but resource allocation, data access permissions, inter-agent communication protocols, and escalation procedures when agents encounter unexpected scenarios. "Policy layers are the AI version of human etiquette," but they must be designed to handle the complexity of system-level operations.
Operational Challenges at Scale
The shift toward autonomous agents also introduces entirely new categories of challenges. As Google's agentic checkout demonstrates, when AI completes transactions autonomously, fundamental questions arise: "Who handles fraudulent purchases? What happens when products don't match descriptions? How do you validate customer identity? Who owns risk management?" Current fraud detection systems are "built for human behaviors, not AI agents acting on behalf of humans," requiring comprehensive model retraining.
The challenge extends beyond individual transactions to system integrity. Kozyrkov emphasizes that "you won't anticipate everything that can go wrong with a performant (i.e. complex and flexible)" AI system. For agentic operating systems managing multiple concurrent agents with broad system access, the potential for unexpected interactions and cascading failures multiplies significantly.
Building Reactive Infrastructure
The key insight is preparation for the unexpected: "You should make it easy to react quickly when (not if) undesirable unanticipated behavior shows up." This means agentic OS architectures must be designed with monitoring, containment, and rapid response capabilities built into their core infrastructure rather than bolted on afterward.
The economic disruption could be massive. Traditional optimization strategies—like A/B testing checkout pages and reducing cart abandonment—may become "irrelevant" as agents bypass traditional user interfaces entirely. Meanwhile, the "first platform to make agentic commerce seamless wins the next decade."
The economic impact could be substantial. Industry experts predict that "2025 is going to be the year we see the first billion-dollar verticalized AI agent companies emerge," suggesting that specialized agentic systems will capture significant market value.
The Path Forward
The hypothesis that we're heading toward agentic operating systems isn't just plausible—it's inevitable. The convergence of several trends makes this transition not a matter of if, but when:
The fintech arms race exemplifies this acceleration, with "OpenAI hiring Instacart CEO Fidji Simo for consumer operations," "Perplexity embedding Stripe within its chatbot," and "Visa, Mastercard and PayPal announcing agentic commerce services in April." The battle for "who owns the moment before, during and after payment has begun," and this competition is driving fundamental architectural changes across the industry.
Agentic AI represents "software designed to interact with data and tools in a way that requires minimal human intervention," with an emphasis on autonomous goal achievement. When this capability is embedded at the operating system level, it transforms computing from a tool we operate to an environment that operates on our behalf.
Conclusion: Computing's Next Chapter
We're witnessing the early stages of computing's next great transformation. Just as the graphical user interface revolutionized personal computing in the 1980s and mobile touch interfaces redefined interaction in the 2000s, agentic operating systems will define the 2020s and beyond.
The traditional file-and-folder paradigm served us well for decades, but it's becoming a constraint rather than an enablement in an AI-driven world. Agentic operating systems promise to liberate us from routine digital tasks, allowing us to focus on higher-level thinking and creativity while intelligent agents handle the orchestration of our digital lives.
The question isn't whether agentic operating systems will emerge, but how quickly organizations and individuals will adapt to this new paradigm. Those who embrace this shift early will gain significant competitive advantages, while those who cling to traditional computing models may find themselves increasingly left behind.
The age of agentic computing is upon us. The only question is: are we ready to let our operating systems become truly intelligent partners in our work and lives?
Resources and Further Reading
This article draws insights from several thought leaders exploring the intersection of AI, commerce, and system architecture:
Cassie Kozyrkov - AI Safety Expert and Former Chief Decision Scientist at Google
Simon Taylor - Fintech Expert and Industry Analyst
Dan Hockenmaier - Technology Strategy and Innovation Expert
Devin Kearns - Agentic OS Systems Expert