MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
Automatically tuning instructions for AI teams that work together
When multiple AI agents work together on a task, their individual instructions (prompts) need to work well not just in isolation, but as a coordinated system. A new framework called MASPO automatically improves these prompts by testing how well each agent's output helps the next agent succeed, rather than optimizing each agent separately. Tests across six different tasks show this approach outperforms existing methods by an average of 2.9 percentage points.
As companies deploy multi-agent AI systems for complex work, getting these systems to actually cooperate effectively has been a major bottleneck—manually writing and tuning prompts for each agent is slow and often produces suboptimal teamwork. MASPO makes this process automatic and more effective, which could accelerate real-world deployment of AI systems handling tasks like research, customer service, or software development that require coordinated reasoning across multiple specialized agents.