AI Agents in Production: What Actually Works

Narrow agents beat general ones

The agents delivering real value today do one job with a small toolset: triaging support tickets, reconciling invoices, drafting responses from a known playbook. Every tool you add expands the space of possible mistakes. Start with the smallest set of actions that completes the workflow, and grow only when the error rate earns it.

Design for failure, not for the demo

• Cap loop iterations and total spend per run so a confused agent cannot burn budget.
• Make destructive actions require confirmation or run in a sandbox first.
• Log every step of every run; agent debugging is impossible without traces.
• Define an explicit escalation path to a human when confidence drops.

Human oversight is a feature

The most successful deployments put humans at the boundaries: an agent drafts, a person approves. This is not a temporary crutch. Review queues generate labeled data about where the agent succeeds and fails, which tells you exactly what to fix and when full autonomy is justified for a given action type.

Measure outcomes, not vibes

Pick one operational metric the agent should move, such as median resolution time or percentage of tickets closed without escalation. Run a holdout where some work still flows through the old process. Agents earn expanded autonomy the same way junior employees do: by compiling a track record on the easy cases first.