Most "autonomous agent" projects fail for a boring reason: someone tries to automate judgment before automating the plumbing. Get the plumbing right first, and the judgment part turns out to be the easy 20%.
This is the shape of a support agent that actually survives contact with real tickets — not a demo that works on three happy-path examples and falls over on the fourth.
Before the agent decides anything, it needs to know what's true: order status, refund policy, account history. Wire up retrieval against your actual systems first. An agent with perfect reasoning and stale data will confidently say the wrong thing — and confidence is the expensive part to walk back.
"An agent's judgment is only as good as the ground truth you hand it. Fix retrieval before you tune the prompt."
Every extra tool is another way the agent can pick wrong. Start with lookup order, issue refund, and escalate to human. Ship that, watch where it actually gets stuck, and add the fourth tool only when you have a real transcript proving it's needed.
Paradoxically, the fastest way to earn trust in an autonomous agent is to make it excellent at knowing when it's out of its depth. A clean handoff — full context, no repeated questions — matters more to the customer than one extra thing the bot could technically attempt.
