The Rise of Agentic Engineering: Harness Emerges
Stop focusing only on the model. Start focusing on the harness.
If you are not the model, you are the harness. This is the new reality of AI engineering.
A raw model is not an agent. It becomes an agent when you give it a harness. The harness provides the tools, the environment, and the feedback loops needed to finish a task.
The math is simple: Agent = Model + Harness.
Industry leaders like OpenAI, Stripe, Google, and Anthropic all agree on one thing. The real engineering work is not picking the smartest model. The real work is designing the scaffolding around it.
What makes up a harness?
Think of it in two ways:
- Guides (Feedforward): These steer the agent before it acts. Examples include coding standards, documentation, and instruction files.
- Sensors (Feedback): These observe what the agent did. Examples include linters, tests, and type checkers.
A good harness uses both. If you only have guides, the agent ignores them. If you only have sensors, the agent repeats the same mistakes.
Key lessons from the frontier:
• The Ratchet Effect: Every mistake the agent makes should become a permanent rule. If an agent fails a test, add a sensor so it never happens again. • Shift Left: Run fast, cheap checks (like linters) immediately. Save expensive checks (like AI reviews) for later. • Legibility: If an agent cannot see a piece of information in its context, that information does not exist. Encode your knowledge into the repository. • Constraints Drive Success: Use a strict architecture. This reduces the variety of choices the model makes, which makes it easier to control.
The gap between what a model can do and what it actually does in your codebase is a harness gap.
Don't wait for a better model to solve your problems. Build a better harness.
Source: https://dev.to/raminjafary/the-rise-of-agentic-engineering-part-5-harness-engineering-emerges-2d9o
Optional learning community: https://t.me/GyaanSetuAi