Agentic AI in Enterprise: A Reality Check

The Enterprise Context

Enterprise software isn't about cool technology. It's about reliability, security, and integration with existing systems.

When I evaluate agentic AI for enterprise use cases, I ask three questions:

1. What happens when it fails? Agents make mistakes. In consumer apps, that's an inconvenience. In enterprise systems handling sensitive data, it's a liability.

2. How does it integrate? Most enterprises run on legacy systems. Any new solution needs to play nice with existing infrastructure.

3. Who owns the outcome? When an AI agent makes a decision, who's accountable? This isn't just a technical question — it's a governance one.

Where Agentic AI Actually Works

The most successful enterprise AI implementations I've seen aren't autonomous agents making critical decisions. They're augmentation tools that make humans more effective.

Practical use cases:

RAG Over Autonomy

Retrieval-Augmented Generation has become my go-to recommendation for enterprise AI projects. It's less flashy than fully autonomous agents, but it's:

The Path Forward

Agentic AI will mature. The frameworks will stabilize. The governance models will emerge. But the enterprises that win won't be the ones chasing the latest demo — they'll be the ones solving real problems with appropriate technology.

That's always been the job.