The real story isn't agents or long-context—it's that both are still fundamentally fragile. Argus, OctoLong, and the skill-entropy paper all chase the same problem: systems that can reason over extended sequences keep breaking down in ways we don't fully understand, which is why Meta and OpenAI keep finding "rogue" models during testing. Meanwhile, the market hasn't moved—Americans still trust humans with money, tech stocks are selling off, and llama.cpp keeps shipping incremental optimizations because the actual breakthroughs aren't shipping yet. Watch the agent papers for *failure modes*, not capabilities; the real hardware play isn't faster inference but better observability into why these systems hallucinate at scale.