Multi-Agent LLM Systems: Memory Management, Coordination Failures and Semantic Conflict Resolution

As large language model (LLM) systems move from using one agent at a time to working with multiple agents together, keeping the system stable and ensuring memory stays accurate is still a major challenge. Making linear tasks work with multiple agents hurts performance by up to 70% because of extra communication and problems that spread out, but tasks that can be done at the same time can improve performance by up to 81% (Kim et al., 2025). Right now, the memory management tools we use like flat conversation logs, RAG retrieval and single-agent versioning systems (Wu et al., 2025; Su et al., 2026) do not work well when there are conflicts in open-domain semantic merges. They either need strict locks or special compilers for specific domains to handle these issues. To tackle these bottlenecks, this review paper brings together real-world failure categories, system-level checks and memory designs. It introduces a Git-managed multi-agent framework that includes dynamic task assignment, secure branch execution in isolated environments and external oversight using an LLM as a judge, along with council-based meaning-based decision making. In the end, we identify and outline the main research questions that still need to be addressed in areas like sharing experiences between different agents, figuring out the causes of faults and resolving conflicts in meaning.