According to God of Prompt on Twitter, most AI agent frameworks still load long-term memory at session start, stuff it into the prompt, and then summarize or compress once the context window fills—causing lossy retrieval and "context rot" where agents lose structured access to flushed knowledge (source: @godofprompt, Mar 2, 2026). As reported by the tweet, after compaction triggers, agents rely on brittle keyword or vector search to surface fragments, but cannot systematically browse prior state, making task planning, compliance traceability, and multi-step workflows unreliable in production. According to the same source, this architectural bottleneck creates business risk by degrading reasoning over time, increasing hallucination rates, and inflating inference costs through repeated rediscovery of facts that already exist in memory. For teams building enterprise copilots, the opportunity is to adopt retrieval-first designs: immutable event logs, hierarchical memory indexes, tool-call provenance graphs, and structured episodic memory with queryable schemas—paired with reversible compression, versioned summaries, and cache-aware planners that page memory in and out deterministically.