Harnessing Harnesses - Climbing the LLM Hills

In my previous LLM-themed post I talked about the use of MCPs and how they have been useful for my offensive bug hunting pipeline. MCPs grant Large Language Models(LLMs) access to additional tools and allow them to gather additional context. This post will explore the creation and importance of harnesses for using LLMs to their full potential while maintaining effective token usage.You'll find plenty of discussion about prompt engineering and model selection, but the orchestration layer around the model doesn't get nearly as much attention(until recently with a few open source projects being released as discussed later in the post). In my experience, that's where the biggest improvements in capability, cost and reliability come from. A highly capable model with no structure around it will still burn tokens like they're going out of fashion on redundant context and repeat work it's already done, producing results you can't verify or reproduce. Picking the right model and ignoring the…