In a new technical deep dive, Shankar from System Base Labs tackles the critical challenge of moving agentic AI from demo to production. The module, part of an ongoing series, emphasizes that systems which work in controlled environments often fail under real-world pressure.
Key lessons include designing for scalability with distributed agents and task orchestration, building fault-tolerant architectures that recover gracefully from failures, and securing systems against prompt injection, tool misuse, and data leakage. The talk also covers balancing cost, latency, and performance by measuring metrics like error rates and cost per task.
The core message: production systems must be built to withstand failure. As Shankar notes, "The question is not if—but how your system responds."