How to build AI agents at scale: a checklist for your LLM

Build and push AI agents to production with Rippling's 4-pillar framework

Most AI agents fail in production — not because the models are bad, but because the architecture wasn’t built to survive it. Brittle routers break on edge cases. Eval suites catch nothing until it’s too late. And teams ship AI slop without knowing it.

Rippling’s AI team built a different kind of agent — one that acts like a seasoned admin, not a chatbot. This checklist captures the four-pillar framework behind it. Upload it directly to your LLM or agent and let it guide your build, decision by decision.

What’s inside

• Pillar 1: AI Product Evolution — The three stages every AI product moves through, and how to know which stage you’re actually in

• Pillar 2: The Deep Agent Shift — Why brittle routers fail and how to build agents that lean into LLM reasoning instead of fighting it

• Pillar 3: Fighting AI Slop with Evals — The eval strategy Rippling uses in production, including how to dogfood your own AI and trace failures before they compound

• Pillar 4: AI as Your Superpower (with Guardrails) — How to enable broad AI adoption across your team while enforcing ownership and preventing drift

Each pillar includes a diagnostic checklist — specific questions to audit your current stack and identify where your agents are most likely to break.