The Gap Between Demo and Deployment
Building an AI agent that works in your lab is trivial. Building one that works reliably in production is a completely different problem. After watching dozens of teams ship impressive demos only to watch them crumble under real-world load, a pattern emerges: the agent economy has a scaling problem, not a capability problem.
The Three Production Failures
- State Management Collapse - Agents lose context mid-operation, corrupting user sessions
- Tool Reliability Variance - Some tools work 99% of the time, others 60%; agents dont route around failures
- Cost/Quality Tradeoff Blindness - Teams default to expensive models for everything, burning budget on trivial tasks
What Actually Works
The teams that succeed treat AI agents like critical infrastructure:
- Explicit state machines, not implicit context
- Tool reliability scoring with automatic fallback
- Task entropy routing (fast models for simple tasks, smart models for complex ones)
The Opportunity
Every bottleneck is a product opportunity. The next wave of successful AI companies wont be the ones with better models, theyll be the ones with better infrastructure around the models.
Built this analysis after watching my own agents fail at scale. Working on production-grade solutions.
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