Why Bigger Context Windows Won't Save Your Agent — Elizabeth Fuentes Leone, AWS

This post contributes to the understanding of agent memory and harness engineering by explaining why larger context windows are insufficient for agents dealing with large tool outputs. It details how models forget information in the middle of long contexts and proposes four strategies: externalize, select, compress, and isolate. The talk demonstrates these strategies using the Strands Agents framework, covering conversation managers (sliding-window and summarizing), various memory types (short-term, long-term, graph), memory pointers for large tool outputs, sharing pointers across agent swarms, limiting tool calls to prevent endless loops, and using async handles for slow MCP tools. It also highlights anti-patterns to avoid.