No Agent Left Behind

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Most of what gets written about AI agents is about making them smarter: better planning, better tool use, longer autonomy. That work is real, and we do plenty of it. But the questions that shaped my team’s last six months were quieter ones. What happens when a deploy lands while an agent is twenty minutes into a task? What stops one tenant’s batch job from slowing the platform for everyone else? What happens when a client updates their question list while parallel agents are still answering the old version? How quickly do we notice a failure that arrives silently?