> In your workflow, who implements the review feedback - the review subagent or the code-writing-subagent?
I think currently my orchestrator - which spins up subagents for impl, research, review, fixes, etc.
I've been working with a single orchestrator per "project" or "feature" or whatever sort of "thing I'm paying attention to" is (I guess...chat?) and that orchestrator is told to use subagents to do any/all of this type of stuff, it's sole purpose is to just communicate with me, instantiate these types of 'work', and tell me about results or ask for input.
I am just now considering changing this though and considering something even heavier, but for the purpose of "cleaning up / polishing" lazy work by a single pass of "build thing X", this sort of orchestrator with an AGENTS.md or skill that says "always do a review pass after you finish a task by spinning up N sequential (or parallel) follow up subagents to review and another M parallel or sequential fix subagents to 'fix' those things should make a meaningful difference, if a person sees the same types of traps that their model falls into repeatedly.
You don't even need to have some complex harness. Just take a stock frontier model and its default cli or harness, ask it to edit its agents file to tell itself to always work in this manner, and you're done.
I think currently my orchestrator - which spins up subagents for impl, research, review, fixes, etc.
I've been working with a single orchestrator per "project" or "feature" or whatever sort of "thing I'm paying attention to" is (I guess...chat?) and that orchestrator is told to use subagents to do any/all of this type of stuff, it's sole purpose is to just communicate with me, instantiate these types of 'work', and tell me about results or ask for input.
I am just now considering changing this though and considering something even heavier, but for the purpose of "cleaning up / polishing" lazy work by a single pass of "build thing X", this sort of orchestrator with an AGENTS.md or skill that says "always do a review pass after you finish a task by spinning up N sequential (or parallel) follow up subagents to review and another M parallel or sequential fix subagents to 'fix' those things should make a meaningful difference, if a person sees the same types of traps that their model falls into repeatedly.
You don't even need to have some complex harness. Just take a stock frontier model and its default cli or harness, ask it to edit its agents file to tell itself to always work in this manner, and you're done.