Multi-Paradigm Agent Interaction in Practice:A Systematic Analysis of Generator-Evaluator, ReAct Loop,and Adversarial Evaluation in the buddyMe Framework
Learn how to integrate multiple agent interaction paradigms in a unified architecture using the buddyMe framework, and apply Generator-Evaluator, ReAct Loop, and Adversarial Evaluation to improve agent performance
- Implement the Generator-Evaluator paradigm using the buddyMe framework to evaluate agent performance
- Configure the ReAct Loop to enable tool-use and memory-augmented interaction
- Apply Adversarial Evaluation to test agent robustness and identify areas for improvement
- Integrate multiple paradigms within a unified architecture to leverage their strengths
- Evaluate and compare the performance of different paradigms using the buddyMe framework
AI engineers and researchers can benefit from this analysis to design and implement more effective multi-agent systems, while product managers can use this knowledge to inform product development and strategy
💡 Combining Generator-Evaluator, ReAct Loop, and Adversarial Evaluation can improve agent performance and robustness
Integrate multiple agent interaction paradigms with buddyMe!
Key Takeaways
Learn how to integrate multiple agent interaction paradigms in a unified architecture using the buddyMe framework, and apply Generator-Evaluator, ReAct Loop, and Adversarial Evaluation to improve agent performance
Full Article
Abstract:
arXiv:2605.16821v1 Announce Type: new Abstract: The rapid evolution of Large Language Model (LLM) agents has produced diverse interaction paradigms, yet few production systems integrate multiple paradigms within a unified architecture. This paper presents a systematic analysis of three principal agent interaction paradigms, including Multi-Agent Orchestration (Generator-Evaluator), ReAct Tool-Use Loops, and Memory-Augmented Interaction, as implemented in buddyMe, an open-source multi-model agent
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