Why delegation beats memory in AI Agents

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We've spent the last 6 months building Seer, an agent engine for enterprise workflows. We’re launching on NYE, but honestly, we’re still in the trenches.While scanning the space, I keep seeing devs (us included, initially) get obsessed with complex "memory" layers and graph-based reflection. In practice, we found they mostly lead to context poisoning and high latency.We pivoted to a "Barbell Strategy": Crisp, lean inter-agent instructions paired with massive, localized "artifact" context for sub-agents that are immediately killed after the task.I’m curious—for those of you building agents in production:Have you found a way to make "long-term memory" actually reliable, or are you also moving

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Website: www.getseer.dev/blogs/lessons-dec-2025