Introduction
Software 3.0 is the term introduced Andrej Karpathy in his recent talk at YC startup school. From his talk it sounded like he is talking about the Superagentic AI’s pillars or something very similar. Recent progress in AI and agent technologies demonstrates that the foundational concepts of Superagentic AI remain robust, affirming the company’s core ideas and principles. The core pillars of the Superagentic AI are Agentic DevOps, Agent Engineering, Agent Experience and Agentic Co-Intelligence. You can read more about these pillar on the superagentic AI site. In this post we will discuss recent talk by Andrej Karpathy at YC Startup school and how his thoughts similar to the Superagentic AI pillars.
Andrej Karpathy’s YC Talk
In his recent YC talk, Andrej Karpathy describes the evolution of software across three eras: Software 1.0 (explicit code written by humans), Software 2.0 (neural networks trained on data), and Software 3.0—where large language models (LLMs) like GPT can be programmed in natural language, making software development more accessible and collaborative. LLMs function like a new kind of operating system, with prompts acting as programs, memory managed via context windows, and the model itself as a CPU. This shift democratizes software creation, enabling “vibe coding” where users build applications through intent-driven interaction rather than code alone. While LLMs are powerful tools, they require oversight due to hallucinations and cognitive gaps. Partial autonomy systems, like Cursor, show the importance of keeping humans in the loop. Karpathy emphasizes the need for new infrastructure, like LLM-native documentation and agent-friendly formats, to support this paradigm—hinting at a future where human-AI co-intelligence defines the development process. In his talk he introduced some ideas that are exactly similar to the Superagentic AI’s Pillers that we explore blow
Software 3.0 = Agentic DevOps
Redesigning the Future of Software — Together with Agents In 2025, software development quietly stepped into a new dimension. While much of the industry was still exploring AI-enhanced coding assistants and debating whether prompt engineering was a fad or a future, a deeper transformation was already underway. At Build 2025, Microsoft gave this shift a name: Agentic DevOps — a fusion of DevOps automation with semi-autonomous agents that not only assist, but increasingly engineer entire software systems. At Superagentic AI, we’d already been exploring the same terrain. Ideas like Agentic Co-Intelligence, Agent Experience (AgentEx), and Agent Engineering had been germinating in our labs long before the label caught on. But with this emerging convergence, the era of Software 3.0 finally has its operational foundation.
Software 3.0 Is Eating the Stack
Andrej Karpathy’s framing of Software 3.0 isn’t just a clever update — it’s a reality check. Today, we live in a layered patchwork of Software 1.0 (classic programming), 2.0 (neural networks and data-defined logic), and now Software 3.0 — where English is the interface, LLMs are the runtime, and agents are the new unit of abstraction. Software 3.0 doesn’t replace everything overnight. It coexists, but also steadily rewrites. And it comes with a new intention: Agent Engineering — the process of designing intelligent, autonomous systems that reason, adapt, and collaborate. These agents aren’t just fancy tools. They’re collaborators — capable of navigating complex toolchains, integrating APIs, managing context, learning from system memory, and contributing to long-lived workflows. They don’t just autocomplete—they operate.
Agentic DevOps: The Backbone of Software 3.0
Software 3.0 needs a new kind of DevOps — not just automated, but agent-augmented. That’s where Agentic DevOpscomes in. Agentic DevOps is the emerging practice of embedding autonomous and semi-autonomous agents across the Software Development Lifecycle (SDLC). From planning and provisioning to testing, deploying, and observing, intelligent agents augment human teams with both precision and scale. These agents don’t just execute scripts—they reason through change requests, refactor code based on observed bugs, test edge cases, and make recommendations with full knowledge of the system state and intent. It’s DevOps—but with a living, thinking layer of agentic cognition threaded throughout.
Agent Engineering: Vibe Coding + LLM OS
Where Software 2.0 relied on training models, Software 3.0 is about designing agentic systems. Agent Engineering is the emerging discipline where software engineers are no longer just writing functions and services—they’re defining autonomous behaviors, memory systems, tool-use capabilities, and safe action spaces for agents. This goes far beyond prompting. It requires new abstractions for context management, long-term memory, reflection, tool orchestration, system prompt learning, and supervision layers. Engineers in this paradigm must think like system designers, cognitive architects, and safety validators all at once. It’s not about building monoliths. It’s about cultivating modular, interoperable agents that can learn, reflect, and collaborate—sometimes independently, often alongside humans.
Agentic Co-Intelligence: Partial Autonomy
We can’t build Software 3.0 with AI in isolation. The future is not just artificial intelligence, but agentic co-intelligence. Today’s LLMs still suffer from “Jagged Intelligence” and “Anterograde Amnesia,” as Karpathy notes — brilliant at some tasks, bafflingly poor at others, and unable to consolidate new learning over time. That’s why partial autonomy is critical: agents should collaborate with humans in fast feedback loops, not replace them outright. This is where Agentic Co-Intelligence becomes essential — a framework for designing workflows where humans orchestrate, validate, and train agents in production systems. Think of it as building software with a team of interns who can scale infinitely, learn on the job, and eventually self-improve. We don’t need agents to be infallible. We need them to be collaborative, adaptive, and self-aware enough to improve within human-aligned boundaries.
Agent Experience: Build for Agents
Just as user experience (UX) defined how humans interact with systems, and developer experience (DevEx) optimized workflows for engineers, Agent Experience (AgentEx) defines how agents perceive, navigate, and succeed in digital environments. In a world increasingly built for autonomous AI, we must design for machine readability, structured metadata, accessible APIs, and standardized reasoning environments. HTML isn’t good enough — agents need llms.txt, API maps, task ontologies, and reflection slots. AgentEx is the design layer of Software 3.0. It’s how we empower agents to move through systems intelligently—not just read screens, but make sense of goals, tools, and safety constraints.
From Vibe Coding to Verified Autonomy
Karpathy’s famous “vibe coding” tweet lit a spark. But building production software with agents can’t run on vibes alone. In Software 3.0, we must bridge the demo-product gap. That means building agents that aren’t just exciting in demos—but reliable in deployment. To get there, we need generation-verification loops, autonomy sliders, and evaluation-first methodologies. We must treat agents like teammates—not just code generators. And most importantly: we must make the entire stack — from CI/CD pipelines to design systems — agent-ready.
Welcome to the Agentic Era
Software 3.0 isn’t just about LLMs. It’s about an entirely new way to build — where intelligent agents are the primary manipulators of code, knowledge, and infrastructure. It’s not just autocomplete. It’s a living software ecosystem where humans and agents co-create. At Superagentic AI, we’re not just riding this wave. We’re helping shape it — through frameworks like Agentic DevOps, and principles like Agent Engineering and Co-Intelligence.
Software 3.0 is here. It thinks. It collaborates. It builds.