What's been the hardest part of moving AI agents into production?
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Building a demo is relatively straightforward, but production is a different story.
Reliability, observability, tool failures, memory, permissions, and cost all become much more important once people depend on AI agents every day.
For those who've deployed AI agents, what ended up being the biggest challenge? Was it the one you expected?
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Wasn't the one I expected. Went in worried about reliability and cost, and those turned out manageable with retries and budgets. What actually slowed us down was observability. A normal stack trace tells you where something broke, but an agent's failure is usually "it did something plausible but wrong three steps ago," and by the time that surfaces downstream you've lost the thread. Ended up logging the full decision trace for every run just so a bad outcome could be traced back to the specific step that went sideways, not just the final output.