Harnesses are basically doing this better than just adding more context. Every time, REGARDLESS OF MODEL SIZE, you add context, you are increasing the odds the model will get confused about any set of thoughts. So context size is no longer some magic you just sprinkle on these things and they suddenly dont imagine things.

So, it's the old ML join: It's just a bunch of if statements. As others are pointing out, it's quite probably that the model isn't the thing doing the heavy lifting, it's the harness feeding the context. Which this link shows that small models are just as capabable.

Which means: Given a appropiately informed senior programmer and a day or two, I posit this is nothing more spectacular than a for loop invoking a smaller, free, local, LLM to find the same issues. It doesn't matter what you think about the complexity, because the "agentic" format can create a DAG that will be followable by a small model. All that context you're taking in makes oneshot inspections more probable, but much like how CPUs have go from 0-5 ghz, then stalled, so too has the context value.

Agent loops are going to do much the same with small models, mostly from the context poisoning that happens every time you add a token it raises the chance of false positives.