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The human sciences are shifting: for the first time, core research tasks can be handed off to machines. AI chatbots increasingly contribute to scientific research, including in the most prestigious publications and in the social sciences. This has spurred optimism that AI could boost research productivity—while also stoking fears about overloaded peer review and a deluge of academic AI slop. But while turn-taking AI chatbots have primarily been used for writing assistance, coding agents could restructure social science research more radically. Agentic coding platforms like Claude Code and Code

Using the toolkit for this specific use case provides multiple benefits: Config-driven workflows The toolkit helps shift the project from a rigid script to a flexible research platform. Instead of hard-coding the interactions between agents, you define the system’s logic—including personas, tools, and constraints—entirely within a YAML configuration. This modularity makes it trivial to swap models for different tasks. For example, you can assign a high-reasoning model to handle hypothesis generation while using a faster, more cost-effective model for the code agent without modifying the underl