Solving the Credit Assignment Problem in Multi-Agent Systems (CANTANTE Framework)

Reddit r/AI_Agents Papers
multi-agent credit-assignment auto-ml prompt-optimization open-source agentic-systems framework

Summary

CANTANTE is an open-source framework that solves the credit assignment problem in multi-agent systems by converting system-level rewards into per-agent update signals, outperforming DSPy-based baselines on coding and math reasoning benchmarks.

Hey everyone, If you are building multi-agent architectures, you have likely run into the cascading failure problem: you adjust one agent's prompt to fix a specific edge case, rerun the pipeline, and a downstream agent suddenly breaks or behaves unpredictably. The structural bottleneck here is **credit assignment**. In a multi-agent loop, performance rewards are typically only observed at the system level (e.g., did the final output satisfy the user request?). However, the parameters governing that behavior live inside individual, localized agents. Without knowing which specific agent contributed positively or negatively to the final global outcome, automating system updates is incredibly difficult. **CANTANTE** is an open-source framework built to solve this by turning system-level rewards into per-agent update signals. # How It Works Instead of treating the agentic pipeline as a single black box, CANTANTE isolates agent contributions through a four-step cycle: 1. **Generation:** Local optimizers propose prompt configurations for individual agents. 2. **Evaluation:** These configurations are evaluated on identical queries to capture explicit reasoning traces and system-level scores. 3. **Attribution:** An attributer analyzes and contrasts these rollouts, isolating and assigning a distinct credit score to each agent based on its performance contribution. 4. **Optimization:** These per-agent signals are fed back into local optimizers (we use CAPO, our prompt optimizer from AutoML 2025) to iteratively refine the prompts. # Benchmark Performance We evaluated CANTANTE against state-of-the-art DSPy-based solutions (GEPA and MIPROv2) across multiple agentic benchmarks: * **MBPP (Coding):** Beats the strongest baseline by **+18.9 points**. * **GSM8K (Math Reasoning):** Outperforms the baseline by **+12.5 points**. * **Efficiency:** Maintains standard inference time cost compared to unoptimized baseline prompts—no heavy token or latency overhead to get the performance jump. As a sole-author PhD student working on AutoML for agentic systems, getting this to a point where it significantly outperforms industry-lab baselines has been a massive grind. The entire framework is fully open-source and free to use. I would love to hear how you are handling optimization and evaluation in your multi-agent setups right now.
Original Article

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