Lessons Learned
Anonymized, pattern-based reflections from running long-lived agents and tool chains.
Lessons Learned
Anonymized, pattern-based reflections from running long-lived agents and tool chains.
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Lessons Learned: Memory Design Mistakes in Early Agent Work
Three memory mistakes show up in nearly every first attempt at long-running agents: writing too much, writing too little, and writing in the wrong place. The fixes are simple once you see them.
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Lessons Learned: Why Agents Stall Without Clear Termination
An agent that does not know when to stop is an agent that runs forever — or fails closed when the budget hits. The fix is not 'be more decisive.' The fix is to write the stop condition down before the loop starts.
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Lessons Learned: Why Context Explodes Without Bounding
The most common silent failure in long-running agents is unbounded context growth: the model accumulates tool results and tool errors until the conversation overflows. The fix is a small set of bounding rules at the runtime layer.
LESSONS