Astra Pro parallel agents: scope tasks before splitting them
Using GPT-6 Astra Pro's parallel agents and a Sakana orchestration paper, the author argues each sub-task needs a written scope and acceptance criteria before fan-out.
parallel agents just went mainstream. OpenAI shipped it in ChatGPT last week: GPT-6 Astra Pro can now split a task across several agents running at the same time, and you can follow each one's progress and open its finished work. The same week a Sakana AI paper on orchestrating coding agents for research made the harder point. Their system runs task drafting, planning, sandboxed experiments, baselines, analysis and report writing as separate agents. A meta-controller runs them in parallel and checks every stage against a rubric. Their ablations showed reliable autonomy depends on that orchestration layer as much as on the model underneath. the default that costs you: handing one agent one giant task and waiting. parallelism without a plan makes the mess faster. what I do before fanning anything out: write the scope and the acceptance criteria for each piece first. what finished looks like, what is out of bounds, what gets checked at the end. then split. an agent with a rubric finishes the job it was given. the check: if two of your parallel agents can't say what the other one owns, the split was wrong. I keep one prompt for exactly this scoping step. it refuses to write a single line until the task is bounded, which is the whole point. Sakana's development version of this system helped write a paper that was accepted as a main-conference talk at ALIFE 2026, after human verification and edits.