Every conference has a session on building AI agents. Most show a demo that works perfectly in a controlled environment. Then you try to deploy it and everything breaks.
The gap between demo and production
Production AI systems deal with inputs that no demo anticipated: edge case documents, adversarial prompts, latency spikes, rate limits, and model updates that silently change behavior. Building robust systems requires treating the LLM as an unreliable component in a larger architecture — not as the entire system.
What actually matters
Structured outputs, always. Never parse free-form LLM text in production. Use function calling or constrained decoding to get predictable, validatable outputs.
Observability from day one. Log every prompt, every completion, every latency metric. You cannot debug what you cannot observe.
Human-in-the-loop for high-stakes decisions. Design your confidence thresholds carefully. When the model is uncertain, route to a human.
Test with adversarial inputs. Build a red-team dataset before launch. Your users will find inputs you never anticipated.