How to Build a Model-Agnostic AI Agent System
Nate Herk | AI Automationgo watch the original →
the gist
To avoid vendor lock-in, treat AI models as interchangeable 'brains' while focusing your effort on building a robust, portable 'harness' of tools, skills, and automation logic.
The Brain vs. The Harness
Nate Herk and Mark Kashef argue that the industry focuses too heavily on which LLM is currently superior. They define the 'brain' as the model itself (Claude, Codex, etc.) and the 'harness' as the surrounding infrastructure—the ability to read/edit files, execute bash commands, and manage agentic loops. A model without a harness is essentially a 'brain in a jar' that can generate text but cannot interact with the physical or digital environment to execute tasks. The harness is what transforms a model from a chatbot into an agent capable of autonomous work.
Building Portability and Avoiding Lock-in
The core strategy for long-term sustainability is to remain loyal to your own 'harness' and assets rather than any specific AI provider. By standardizing skills and project structures, users can swap out the underlying model as better options emerge without having to rebuild their entire workflow. This involves creating 'model-agnostic' skills that are optimized for multiple environments, ensuring that logic remains portable across different agentic frameworks.
The Agentic Loop and Verification
Different models excel at different stages of the agentic loop. Herk and Kashef describe Claude Code as a 'wise old owl'—excellent for planning, ideation, and high-level strategy—while characterizing Codex as a 'Rottweiler'—a high-precision tool that excels at following strict instructions and executing verification loops. A sophisticated workflow often involves making these models 'fight' in a loop, where one plans and the other verifies, ensuring that the final output is robust and error-free.
Maintaining an AI Operating System
As AI tools evolve, skill repositories often become bloated with unused or outdated functions. The speakers recommend a regular 'audit' of your AI ecosystem. This includes using AI to reverse-engineer the documentation of new harnesses (like Pi or Hermes) and using automated scripts to refactor existing skills for compatibility. By treating your AI setup as a living operating system that requires maintenance, you prevent technical debt and ensure that your agents remain efficient as models improve.