Everyone's Wrong About AI Agents in Production
The AI agent hype is everywhere. Every vendor is selling you the dream of autonomous systems that will revolutionize your business. But here's the truth: most of what you're hearing is noise.
The Reality Check
After deploying AI systems in production at BNZ, I've learned what actually works versus what just sounds good in PowerPoint presentations. The gap between demo and production is massive.
What Actually Works
- Narrow, well-defined tasks - Agents excel when they have clear boundaries and success criteria
- Human-in-the-loop workflows - The best systems augment humans, they don't replace them
- Robust error handling - Production AI needs to fail gracefully, not catastrophically
What Doesn't Work
- Fully autonomous decision-making in high-stakes scenarios
- Black-box systems without explainability
- One-size-fits-all solutions from vendors
The Implementation Gap
The difference between a demo and production is:
- Monitoring and observability - You need to know when things go wrong
- Fallback mechanisms - What happens when the AI fails?
- Cost management - Token costs add up fast at scale
What You Should Do Monday Morning
Start here on Monday:
- Identify one repetitive task that has clear success criteria
- Build a prototype with human review at every step
- Measure accuracy and cost for 2 weeks before scaling
- Document failure modes and build fallbacks
Don't try to boil the ocean. Start small, measure everything, and scale what works.
The Bottom Line
AI agents can deliver real value, but only if you approach them with clear eyes. Focus on augmentation, not replacement. Build in layers of safety. And for the love of all that's holy, measure everything.
The hype will fade. The practical implementations will remain. Be on the right side of that divide.
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