What I Learned Building AI Agents for Real Businesses

The gap between a demo-ready AI agent and one that can run in production for a real business is enormous. Here's what actually matters when deploying agents in enterprise environments.

Demo vs. production

In a demo, you control the inputs, the context, and the environment. In production, users do weird things, data is messy, and edge cases are the norm. The first lesson: plan for failure modes from day one. Retries, fallbacks, and clear error states aren't nice-to-haves — they're table stakes.

What enterprises care about

• Security and compliance — where does data go, who can see it, and how is it retained?
• Reliability — can we depend on this for mission-critical workflows?
• Observability — when something goes wrong, can we debug it?

The unglamorous work

Most of the work isn't the model or the prompt — it's integration, testing, and iteration. Building agents for real businesses means embracing that unglamorous work and treating it as core to the product.