Mastering Production AI: Beyond Prototyping to Reliable SolutionsMastering Production AI: Beyond Prototyping to Reliable Solutions
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I shipped an AI agent to production for a real business (a pharmacy). Not a demo. Not a prototype. It runs their back-office operations every day.
Here's what nobody tells you about production AI agents:
The agent is 20% of the work. The other 80%: permissions, guardrails, error handling, and designing what happens when the AI is wrong. Because sometimes it's wrong.
Tools > prompts. My biggest quality jumps didn't come from better prompts — they came from better tool design. The agent has 12 custom tools, and each one is small, boring, and does exactly one thing. Boring tools = reliable agent.
You need evals before you need users. I built an adversarial test suite that attacks my own agent (prompt injections, out-of-scope requests, edge cases) and measures how often it breaks. Every release runs against it. Without this, "it seems to work" is all you have.
Real businesses don't want AI. They want outcomes. The owner never asked "which model is it?" They asked "did the orders get processed?" That reframing changed how I build.
Production means someone depends on it. An agent that works in a demo and an agent a business relies on daily are two different products. The gap between them is where the real engineering lives.
Building one of these for your business or product? Happy to share what worked (and what didn't) — ask me anything below.
#AI #AIAgents #ClaudeAPI #NextJS #BuildInPublic
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