QA for AI agents and chatbots

The measurement stack that shows whether your AI agents really resolve problems, and which popular numbers to stop putting in front of leadership.

Your AI agent closes tickets alone and its worst answers never escalate. How to find out if it is doing damage: what to sample first, and what to score.

Deflection rate counts the tickets your AI agent closed, not the ones it closed well. How to audit the deflected population and find the silent failures.

A taxonomy of the AI agent errors your dashboard records as wins, with the signals that expose each one before customers stop trusting you.

What to score in both directions of the handoff, the failures that hide at the seam, and how to catch them when per-agent scores look fine.

Which criteria to add, which human ones to drop, and how to score an AI agent fairly without letting it mark its own homework.

How to measure containment honestly, pair it with a resolution check, and stop a hidden escalation path from flattering your AI agent's numbers.

What containment rate measures, what it quietly leaves out, and why a contained conversation is not the same thing as a resolved one.

How to measure AI agent performance in customer service: the metrics that matter, scoring against a scorecard, fair AI vs human comparison.

Agentic AI is AI that acts autonomously toward a goal, taking multi-step actions without step-by-step prompting. Here is what that means for customer service.

An AI agent is autonomous software that handles customer interactions end to end. Here is how it works and why its quality still needs to be measured.

Real-time agent assist is software that guides agents live during a conversation with suggested responses, next steps and knowledge. Here is how it works.