Show HN: Why delegation beats memory in AI Agents
getseer.devWe'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 toward ephemeral, specialized agents?
What’s the "boring" plumbing problem (Auth, state rollback, etc.) that took you way longer to solve than the actual AI logic? While building GTWY, we found delegation scales better than memory-heavy agents. Passing clear responsibilities between steps stayed more reliable than trying to make agents “remember everything.” How are you storing the agent context now?