AI Support

How to stop an AI support agent repeating old answers after a release or policy change: one source of truth, retiring docs, reindexing, tests, vendor checks.

Zendesk shipped a Context Graph capturing an audit trail of agentic reasoning. Why explainability moved from nice-to-have to procurement question, and what to ask.

A quality framework for customer-facing AI support: what to sample, what to score, and the metrics that make the others trustworthy.

A realistic one-day plan for going live with AI support: what to prepare, what to configure, what to test, and what to leave for week two.

A pre-deployment checklist for support documentation, because a grounded AI system reproduces your content faithfully including its errors.

Escalation design decides whether AI support feels helpful or hostile. Here is what a good handoff carries, when it should fire, and what to route by rule.

What AI handoff means, the five triggers that should fire it, copyable handoff messages for customers and agents, and how to measure whether handoffs work.

A pilot design that produces a decision rather than a feeling: scope, success criteria, measurement, and the traps that make pilots meaningless.

Customers do not hate chatbots, they hate being trapped by one. The specific design decisions that cause the reaction, and how to reverse them.

Agent assist improves efficiency per contact. Customer-facing resolution changes how many contacts a human touches. Here is how to choose and sequence them.

The specific questions that separate AI support vendors quickly, covering grounding, escalation, billing definitions, spend caps, and data handling.

What to ask about model training, data residency, retention, and sub-processors before putting customer conversations through an AI system.

A practical sequence for introducing AI into a support function: what to automate first, what to leave alone, and how to tell whether it is working.