The Real Agent Failure Mode Isn't Hallucination — It's Context Drift, Say Operators Running Them for Months
Practitioners who have kept autonomous agents alive in production for the better part of a year are converging on an uncomfortable consensus: the hard problem isn't model intelligence, it's memory hygiene, deterministic fallbacks, and governance no one budgeted for.
The most useful AI engineering conversation of the day had nothing to do with a new model. It came from the people quietly keeping autonomous agents running in production, and their message was blunt: the thing that breaks agents is not what most teams think.
"I've been running an autonomous agent for 8 months," wrote @johniosifov. "The biggest failure mode isn't hallucination. It's context drift." His fix is almost aggressively unglamorous — hard caps on the agent's working memory. State files capped at 200 lines. Individual memory files capped at 10KB. The implication is that an agent's degradation over long horizons is less an intelligence problem than an entropy problem: left unmanaged, the accumulated context slowly poisons every subsequent decision.
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