89Your agent calls two tools and gets conflicting answers. How does it decide which to trust?▼hardAnthropicSierraSalesforce◆ premiumThe CRM says the balance is $0 and the ledger says $412. An agent left to its own devices will average them into something confident and wrong. Designing the precedence before the model has to guess is the whole answer.Open full answer →
14Design a multi-agent customer support system with escalation to humans.▼hardSierraDecagonSalesforce1 replies○ sign inSupport is the flagship applied-AI use case, exercising agents, RAG, tools, and the critical human handoff. The signal is knowing when to answer, when to take action, and when to escalate, safely.Open full answer →
90Design an LLM-based content moderation system that screens user content at platform scale.▼hardOpenAIMetaGoogle1 replies◆ premiumModerating billions of items a day with an LLM on each one is too slow and too expensive. Learn the tiered funnel (cheap classifier then LLM then human), how to tune thresholds for precision versus recall, and how to stay ahead of adversaries.Open full answer →
93Design a customer-support automation platform that resolves tickets end to end with LLMs.▼hardSierraDecagonSalesforce2 replies◆ premiumAuto-resolving support tickets means grounding answers in your knowledge base, safely calling real APIs (refunds, account changes), and knowing when to hand off to a human. Learn the agent loop, the guardrails, and how to measure resolution without eroding trust.Open full answer →