How are teams productionizing AI agents today?

Question

Many AI agent demos work well in prototypes but break when moved to production.We&rsquo;ve been exploring how teams are running agentic AI systems reliably on AWS, including orchestration, scaling, monitoring, and guardrails.We&rsquo;re hosting a live technical masterclass where we&rsquo;ll walk through architectures and real-world patterns used in production environments.If this topic is useful for engineers here, they can join the session.Event link - https://www.netcomlearning.com/webinar/productionizing-agentic-ai-on-aws-from-poc-to-enterprise-scale?utm_source=newplatform_event&utm_medium=event&utm_content=master_classes&utm_term=hnAre you curious how others here are running AI agents in production today?

raffaeleg · Accepted Answer

The biggest gap between demo and production isn't the model or the framework. It's three boring things: deterministic fallbacks (what happens when the agent fails or hallucinates), observability (can you trace exactly why an agent took action X), and cost controls (token budgets per task, not per call). Most teams get burned by the same pattern: the demo works beautifully, then in production you realize you need to handle the 15% of cases where the agent confidently does the wrong thing. The teams shipping successfully treat agents like junior employees, not autonomous systems. Guardrails first, autonomy second.