EVOLVING ARCHITECTURES AND LONG-HORIZON PLANNING IN MULTI-AGENT CONVERSATIONAL AI: A DECADE IN REVIEW
Rohan Mandar Salvi, Pronob Kumar Barman · The American Journal of Interdisciplinary Innovations and Research · 2025
This systematic review surveys advances in conversational AI from 2015 to 2025, focusing on the emergence of modular multi-agent architectures, hierarchical reinforcement learning, and self- evolving agents. A quantitative synthesis of 63 studies indicates that memory-augmented, long- horizon planners improve task success rates by approximately 30% over flat policies, while meta- learning and lifelong learning approaches halve sample complexity in data-scarce domains. Despite these gains, current systems remain brittle under distribution shifts, lack principled safety guarantees, and provide few benchmarks for diagnosing co-adaptive failure modes in mission-critical applications.