Deploying AI agents in production is fundamentally different from building a demo. After 50+ enterprise automation projects, here is what we have learned.
What actually works
AI agents excel at tasks with well-defined inputs and outputs: document processing, data extraction and transformation, report generation, and rule-based decision support. These are not glamorous use cases, but they deliver immediate, measurable ROI.
What fails
Open-ended reasoning tasks, anything requiring real-world judgment under uncertainty, and multi-step processes without clear checkpoints are where agents break down. The failure modes are subtle — agents confidently produce wrong outputs, which is worse than producing no output.
The architecture that works
The most reliable enterprise AI automations we have built share three characteristics: narrow scope, deterministic validation gates, and full audit trails. Build for reliability first, capability second.