Shipping AI agents to production without losing sleep

AI agents are everywhere in 2026 — but most teams still hesitate to put them in front of real customers. The fear isn't unreasonable: autonomous software that can take actions also makes mistakes. The good news is that shipping agents safely is an engineering problem, not a research problem. Here's the playbook we use.

Start with a narrow, high-value task

The biggest mistake is building a general-purpose assistant. Instead, pick one task that's valuable, frequent and verifiable. A great first agent resolves tier-1 support from your help center — bounded, measurable and easy to evaluate.

Ground it in your data with RAG

Hallucinations almost always come from missing context. Retrieval-augmented generation — feeding the model only your verified documents — dramatically improves accuracy and makes answers attributable.

const answer = await agent.invoke({
  question,
  context: await retrieve(question, { topK: 6 }),
  tools: [searchOrders, createTicket],
});

Add guardrails and a human in the loop

For anything that takes a real action — a refund, a status change, an outbound message — require a confidence threshold and a human checkpoint. Log every decision so you can audit and improve.

• Confidence threshold before any irreversible action
• Full audit log of inputs, outputs and tool calls
• Human approval queue for edge cases
• Rate limits and cost caps as a safety net

“Treat your agent like a junior employee: give it clear instructions, supervision on hard calls, and a feedback loop to get better.”

— Aarav Mehta, AI Lab Lead

Measure, then ship more

Define success metrics before launch — resolution rate, escalation rate, customer satisfaction. Once an agent is reliably handling its narrow task, expand its scope one step at a time. That's how you get from a demo to dependable.