The promise of AI agents is compelling: autonomous systems that can handle complex workflows, make decisions, and take actions without constant human oversight. The reality is messier—but with the right approach, these systems can deliver tremendous value.
Start with the Right Use Cases
Not every problem needs an AI agent. Before building, ask: Is this task repetitive? Does it have clear success criteria? Can mistakes be caught and corrected easily? The best agent use cases have high volume, clear patterns, and forgiving failure modes.
Design for Failure
AI agents will make mistakes. The question isn't if, but how often and how you'll handle it. Build in human escalation paths, logging, and monitoring from day one. Your agent should know when it's uncertain and when to ask for help.
Iterate Relentlessly
The first version of your agent will be mediocre. That's okay. What matters is having the instrumentation to understand how it's performing and the architecture to improve it quickly. Ship, measure, improve, repeat.