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.

Productivity tools have survived due to high user switching costs. Agentic AI presents the first major disruptive threat by creating an abstraction layer that can access data and perform actions across any tool, making the underlying application itself far less important.

Open-source agent frameworks like OpenClaw allow users to retain ownership of their data and context. This enables them to switch between different LLMs (OpenAI, Anthropic, Google) for different tasks, like swapping engines in a car, avoiding the data lock-in promoted by major AI companies.

In architectures like OpenClaw, an agent's state and memory are stored in a file system, not the model itself. This means your agent is its files. You can swap the underlying LLM and the agent retains its identity and capabilities, much like recompiling code for a new chip.

The friction of switching AI chatbots comes from losing the model's accumulated knowledge about you. This "context lock-in" makes users hesitant to start over with a new system. A portable, personal context portfolio is the key to breaking this dependency and maintaining user sovereignty over their AI relationships.

Reusable instruction files (like skill.md) that teach an AI a specific task are not proprietary to one platform. These "skills" can be created in one system (e.g., Claude) and used in another (e.g., Manus), making them a crucial, portable asset for leveraging AI across different models.

Instead of explicitly telling an AI agent how to organize its knowledge, simply provide the necessary context. A well-designed agent can figure out what information is important and create its own knowledge files, such as a 'user.md' for personal details or an 'identity.md' for its own persona.

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.