IMO a core hard part of research automation seems to be adapting plans on the fly.

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Research agents need to operate on long horizons under uncertainty.

• Often I have a big experiment made of several stages, and the outcome of each stage is uncertain.
• If stage one turns up a surprising result, running the rest as planned might be pointless — the right move is to reorient and update.
• I want agents that can carry out the long-horizon plan, but also notice when something surprising happens and respond intelligently.

So: how should an agent update when it hits a surprise?

• Option 1 — stop. Something unexpected happened, so halt and wait for instructions. This is safe — it caps the amount of tokens wasted on something that might be useless.
• Option 2 — proceed, but update the plan intelligently. Harder. This requires commander's intent: the research North Star needs to be specified up front, and the agent looks for a way to pivot that still makes progress toward it. Doing this well requires rich context about what you're actually trying to do at a high level, let it pivot, and have it document its decisions along the way so you can ask about them later.

Option 2 is much closer to how I'd treat a human mentee. I suggest some experiments; they go off for a few days with low visibility; and when I check in they've often hit problems or done something different from the plan — but a good mentee will have made real progress anyway.

I'm not sure how to build this yet but it seems very valuable to have.