Agent 长任务上下文工程解析：用预算控制、压缩、todo-state 和记忆对抗上下文溢出与目标丢失

The article examines four mechanisms—context budgeting, compaction, memory strategy, and todo-state—that enable AI agents to handle long-horizon tasks by preventing context overflow and goal loss. Rather than relying on larger context windows, effective agent harnesses implement offloading rules, truncation thresholds, and memory management to maintain task focus across hundreds of tool calls.