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Shipping LLM agents that survive production
Vysync AI Squad·July 22, 2026·8 min read
Evaluation harnesses, guardrails and fallback paths — what separates a demo from a dependable AI agent.
Beyond the Prototype
Building a proof-of-concept LLM agent in a notebook takes an afternoon; shipping one that handles hundreds of edge cases with zero hallucinations in production requires rigorous engineering discipline.
Production reliability demands three foundational pillars: automated regression test harnesses, multi-tier schema guardrails, and deterministic fallback routines when inference endpoints fail.
Production Ready
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