Moving AI from demo to production (a few architecture pitfalls to avoid)
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spent the last few years building ai infrastructure across startups and mncs. one major pattern i see with early-stage teams right now is over-engineering complex agentic loops before fixing their basic tool-calling data structures. you usually just end up burning massive api costs on infinite reasoning loops.
if you're a local founder building an mvp or trying to automate internal workflows, happy to act as a technical sounding board or sanity-check your roadmap to save you some development overhead.