Instead of interacting with a single LLM, users will increasingly call an API that represents a "system as a model." Behind the scenes, this triggers a complex orchestration of multiple specialized models, sub-agents, and tools to complete a task, while maintaining a simple user experience.

Simply offering the latest model is no longer a competitive advantage. True value is created in the system built around the model—the system prompts, tools, and overall scaffolding. This 'harness' is what optimizes a model's performance for specific tasks and delivers a superior user experience.

Major AI platforms are becoming "super agents" that connect to a user's software (e.g., email, YouTube) and use "skills" to perform complex, autonomous tasks. This convergence means the choice of platform is becoming a matter of user interface and integration preference rather than unique functionality.

An AI coding agent's performance is driven more by its "harness"—the system for prompting, tool access, and context management—than the underlying foundation model. This orchestration layer is where products create their unique value and where the most critical engineering work lies.

Early agent development used simple frameworks ("scaffolds") to structure model interactions. As LLMs grew more capable, the industry moved to "harnesses"—more opinionated, "batteries-included" systems that provide default tools (like planning and file systems) and handle complex tasks like context compaction automatically.

The trend of AI apps becoming "everything apps" is not a sign of product confusion or desperation. It's a recognition that the ability to write code is the foundational skill for all knowledge work. An agent that can code can also create presentations, analyze data, and build apps, blurring the lines between specialized tools.

The focus in AI has shifted from crafting the perfect prompt (prompt engineering) to providing the right information (context engineering), and now to building the entire operational environment—tooling, systems, and access—that enables a model to perform complex tasks. This new paradigm is called harness engineering.

Top-tier language models are becoming commoditized in their excellence. The real differentiator in agent performance is now the 'harness'—the specific context, tools, and skills you provide. A minimalist, well-crafted harness on a good model will outperform a bloated setup on a great one.

The planned Superapp combining coding, browsing, and chat is more than a UI consolidation. The deeper, more critical goal is to merge multiple backend systems into a single, unified 'AI harness' that manages context, actions, and interaction loops. This creates a powerful, efficient AI layer for various applications.