LLM Agents Lose Goal Coherence in Long-Running Sessions
Developers building multi-step LLM agents report that models drift from their original task framing over extended sessions, abandoning planned workflows or producing outputs that deviate from agreed specifications. The problem is particularly acute with architect-style sub-agents expected to maintain consistent behavior across many turns. No reliable mechanism exists to detect or correct drift without full session restarts.
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No Established Patterns for Running Multi-Agent AI Pipelines in Production
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