AI · June 2, 2026 · 5 min read
Where AI Agents Actually Help in Production
Ahmad Shahzaib
AI agents get pitched as a drop-in replacement for entire workflows, but in production the useful pattern is narrower: a well-scoped agent handling one clear task — triage, summarization, first-draft generation — with a human reviewing the output before anything ships.
Across a handful of client integrations, the projects that succeeded had three things in common: a tightly defined task, a fallback path when the model is wrong, and logging good enough to catch drift early. The projects that struggled tried to hand the agent too much autonomy too early.
The practical takeaway: start narrow, measure constantly, and expand scope only once the narrow version has proven reliable for weeks, not days.
Across a handful of client integrations, the projects that succeeded had three things in common: a tightly defined task, a fallback path when the model is wrong, and logging good enough to catch drift early. The projects that struggled tried to hand the agent too much autonomy too early.
The practical takeaway: start narrow, measure constantly, and expand scope only once the narrow version has proven reliable for weeks, not days.