Long-horizon AI agents that run for hours or days face four core failure modes: context rot, memory drift, goal coherence loss, and error compounding. These aren't model problems — they stem from lacking proper external memory infrastructure. A cognitive-psychology-inspired framework breaks agent memory into four types: working, episodic, semantic, and procedural. Production patterns like checkpoint-and-resume, plan-then-execute, append-only event logs, context isolation with subagents, and causal event graphs help keep agents on track. Redis Iris is presented as an integrated platform providing durable state, fast retrieval, and data freshness through Agent Memory, Context Retriever, Data Integration, LangCache, and Redis Search — all on an in-memory architecture designed to minimize latency compounding across long agent runs.
