We are engineering harnesses to make AI agents more capable

We are engineering harnesses to make AI agents more capable.
We should also engineer them to make better use of resources.

That is the idea behind Sustainable Harness Engineering.

As agentic AI evolves, the model is only one part of the system. The harness around it increasingly shapes how the agent behaves: the context it receives, the tools it can use, the constraints it follows, the actions it can take, and the feedback it receives.

So why not build sustainability into that same layer?

A sustainable harness could help agents understand the resource implications of their decisions across energy, carbon, water and waste.

The important part is not simply minimizing one metric. It is understanding the trade-offs.

A smaller model may consume fewer resources but require more retries. Caching may reduce computation while increasing storage. A lower-carbon location may have different water implications. More agents may improve reliability while increasing overall resource use.

This is where harness engineering becomes particularly interesting.

The harness can provide agents with sustainability context, resource signals and feedback, alongside the mechanisms we already design for reliability, security, performance and autonomy.

The question becomes:
What outcome are we trying to achieve, what resources are required to achieve it, and what trade-offs are we making along the way?

As harness engineering becomes a core part of agentic AI architecture, sustainability should be built into it from the beginning.

That is Sustainable Harness Engineering.

I’ll explore what this could look like in practice in my next Technology Bytes issue.