What Breaks in AI Agent Memory After Months in Production?

How does agent memory hold up after months of production use? I'm researching how teams handle long-term memory for AI agents, and I'm particularly interested in what happens after the basic memory setup works. For example, early on, storing and retrieving memories seems fairly straightforward. But after months of interactions, I imagine you start dealing with things like: Old information that is no longer true Multiple memories about the same entity Conflicting information from different sessions/agents Knowing which version of a fact is current Relationships between entities becoming important Deciding what should be retained vs discarded Sharing knowledge across multiple agents For those actually running agents in production: What has become difficult about memory as the system has grown? Do you use something like Mem0, Zep, LangGraph, a vector DB, a knowledge graph, or a custom system? And if you're using a memory framework, what did you still have to build yourself? I'd especially like to know about things that actually broke or became painful in production.