[AI]January 30, 2026·12 min read
Taking AI Agents to Production
Demo-grade agents collapse in production. Engineering patterns that close the gap.
AI Lab
AI · Research
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Demo-grade agents collapse in production. Engineering patterns that close the gap.
AI agents look great in demos. But production — knowing when to stop, recovering from errors, controlling cost — surfaces hard problems. This piece shares what we've learned.
A demo agent and a production agent are not the same code — they're not the same problem.
Tool calling looks reliable until it isn't. LLMs invent parameters, hallucinate tools, get stuck in loops. Schema validation, retry policy and a human-in-the-loop safety net are required.
Cost should be tracked per agent. If a task should finish in 50 tokens but spends 5,000, you need an early signal. Use token budgets and a circuit breaker pattern.
We shipped dozens of LLM-backed products. Which patterns scaled, which collapsed — field notes from production.
Next.js 16, Tailwind v4, server components, the edge runtime — what should you actually build a web product with in 2026?