> Fixing unreliable agents by adding more unreliable agents.

Depending on how you combine imperfect systems, you can lower or increase error rates.

> data they have learned about the world, instructions and data under analysis.

I don't think there really is a clear distinction between these things though.

And I don't mean this in a gotcha way - these things apply to people too. Not that that means they're like people, but we have successfully built enormous systems out of fallible people, and other imperfect things.

The goal is rarely "eliminate all errors" but "have a thing that's useful, given a possibility of things going wrong".

Yes, also I hate the direction "frontier" is taking now. Opus 5.5 may be better in agentic coding and reverse engineering, but it has regressed greatly in instruction following.

You can give it instructions and 5k tokens later it will do exact opposite you told it to.

> Fixing unreliable agents by adding more unreliable agents.

I think the 'reliability add' is from how it's presented back to the user. If you can trust the agent to at least say what tool it called, then you can mark a chart as being the canonical output of "get_projected_profit" tool vs "get_realised_profit" tool and everyone knows it hasn't come down to the current dice roll of randomness what the agent itself did this time.

They are attempting to fix an inherent problem with the technology via epicycles.

Reliable TCP was built on top of unreliable IP. Our body chemistry is another example of unreliable non-deterministic processes creating more deterministic outcomes. Yes, yes, it's all different. The common idea is the same: we can get reliable results out of unreliable systems.

> You can ask them only to consider the 5 facts in front of them, but they won't do that, and cannot do that.

Has training them to do it been tried and proven to fail?