The Demo vs. Reality Gap
Everyone’s talking about AI agents. Demo videos show agents booking flights, managing calendars, and solving complex problems autonomously. But in the enterprise? The reality is more nuanced.
What Actually Works
After building multiple production AI systems, here’s what I’ve learned works:
1. Constrained Problem Domains
AI agents excel when:
- The scope is well-defined
- Success criteria are measurable
- Failure modes are understood and acceptable
2. Human-in-the-Loop Design
The most successful implementations include:
- Clear escalation paths
- Verification checkpoints
- Audit trails for compliance
3. Integration with Existing Systems
AI agents don’t replace your tech stack—they orchestrate it:
- API connectivity is critical
- Error handling matters more than in traditional systems
- Monitoring and observability are essential
What Doesn’t Work (Yet)
Fully autonomous decision-making in high-stakes scenarios. The technology isn’t there, and more importantly, organizations aren’t ready for it.
One-size-fits-all solutions. Every enterprise has unique workflows, data structures, and constraints.
Building for ROI
When I design AI agent solutions, I focus on:
- Measurable value: Automate tasks with clear time/cost savings
- Incremental deployment: Start small, prove value, expand
- Sustainability: Build systems teams can maintain and improve
The Path Forward
AI agents will transform enterprise operations—but gradually, not overnight. Success comes from:
- Understanding your specific use cases
- Building with production realities in mind
- Measuring and iterating based on actual results
Want to discuss practical AI agent implementation for your organization? Get in touch.