𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗜𝘀 𝘁𝗵𝗲 𝗛𝗮𝗿𝗻𝗲𝘀𝘀, 𝗡𝗼𝘁 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹
Every AI system has two parts: the model and the harness.
The model provides raw reasoning. The harness is the code that wraps it.
Claude Code, GitHub Copilot, and ChatGPT are not models. They are harnesses. They provide the tools, memory, and loops that turn a model into a useful product.
Use this formula: Agent = Model × Harness
The model is an ingredient. The harness is the dish.
Most engineering work happens in the harness. You do not train the model. You wrap the model. You decide which tools it uses, how it handles errors, and how it remembers context.
Right now, companies like OpenAI and Anthropic build both parts. They build the engine and the car. This will change.
The future belongs to model-agnostic harnesses.
A coding harness needs access to repositories and test loops. A legal harness needs citation accuracy. A finance harness needs audit trails. The model stays the same, but the harness changes based on the job.
Harness engineering will absorb most of current software engineering.
The work shifts from writing deterministic logic to building systems that automate human workflows.
This does not mean AI replaces all code.
- Core business processes like payments and ledgers must stay deterministic. You do not want a model guessing your accounting.
- Human-driven tasks like triage and workflow management will move to agents.
The job changes for developers.
ML experts will focus on the model side. Developers will focus on the harness side.
The harness is where you decide on safety, cost, and reliability. A better model with a bad harness is just a faster way to fail.
The model improves on its own. Your success depends on how you build the harness.
Optional learning community: https://t.me/GyaanSetuAi