What actually broke moving agents from demo to production: 5 lessons
No-Age-3362 · reddit · 2026-10-21
A developer building agentic workflows for clients lists what only showed up with real users:
- Tool calls fail quietly — agents carry on confidently on empty or half-broken API responses. Now every tool output is validated before reasoning.
- Long chains drift — around step 6-7 the agent forgets the original ask. Restating the goal at checkpoints beat any prompt tweak.
- Evals were an afterthought — keeping a small set of real production failure cases and rerunning them on every change catches regressions early.
- Retrieval was the real problem, not the model — half of "hallucinations" were faithful answers from the wrong chunk; fixing chunking and adding a relevance check did more than switching models.
- Users want an undo button more than autonomy — human approval on risky steps built more trust than a smarter agent.
Related event: Five Common Pitfalls When Taking AI Agents from Demo to Production(2 posts)→
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