Tenant isolation for an AI agent means picking a database model and then enforcing it again inside agent memory, traces, and the dashboard your own customers see. Here's how to choose.
AI agent benchmarks like AgentBench and tau-bench tell you what a model can do in a lab. They don't tell you if your agent is actually working for your paying customers. Here's the gap and how to close it.
Prompt versioning tracks changes to an AI agent's prompt, but an eval score that passes on average can still hide a regression that only shows up in one tenant's deflection rate.
AI agent hallucination is not always a wrong fact. Often it's a confident claim that no tool call ever backed up, and it skews the deflection rate you report to customers.
Customer-facing analytics for an AI agent means real numbers scoped to one customer, not a copy of your internal dashboard. What to show, hide, and build.
Human in the loop AI agents need a real escalation rule: confidence thresholds, deflection rate math, and the multi-tenant catch most guides skip.