
Building and maintaining AI agents requires significant product-savvy oversight, as "set-and-forget" deployment leads to inevitable production failures, model-based regressions, and data drift. While non-technical users can now create functional applications using low-code platforms, ongoing maintenance remains a critical, under-discussed challenge. Effective agentic workflows, such as automated customer success and marketing outreach, succeed by ensuring no lead is left behind, yet they require constant human monitoring to correct hallucinations, manage API token expirations, and handle complex logic like multi-language localization. Even sophisticated tools like Salesforce’s integrated agents require careful configuration and human orchestration to avoid costly errors. Ultimately, the transition to agentic operations demands a shift from passive reliance to active, daily management of AI performance to ensure accuracy and operational continuity.
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