I’ve been building AI agents for businesses recently and I think most people are overestimating autonomy and underestimating reliability.

A lot of agent demos look impressive for 5 minutes. But the real challenge starts when the system has to operate consistently in real business environments: \- messy customer inputs \- incomplete data \- API failures \- unpredictable user behavior \- human interruptions \- edge cases nobody planned for One thing I learned very quickly: Businesses don’t care how “smart” the agent is if they can’t trust it. A simple workflow that works 99% of the time is usually more valuable than an advanced autonomous system that breaks under pressure. I’ve actually started designing agents differently now. Instead of asking: “How autonomous can this become?” I ask: “How stable can this become?” That shift completely changed how I build: \- memory handling \- fallback logic \- human escalation \- tool permissions \- error recovery \- conversation structure Ironically, the more serious the business, the less they want “fully autonomous.” They want controlled intelligence. Feels like we’re entering a phase where operational design matters more than model capability. Curious how other builders here are approaching this.