Measure, or waste your time. How are you measuring answer quality in your AI agents?
Salesforce just dropped a behind-the-scenes breakdown of how they track response quality across their own Agentforce deployments. This isn't about support bots — it's about operationalizing agent intelligence in sales, service, and beyond, and building a quality loop that compounds over time.
The system in one page
- Agent responses are scored on accuracy, relevance, completeness, and trust
- Feedback is structured using prompts, flows, and metadata — not vibes
- Answer quality isn't just tracked — it's governed and improved with real metrics
Three questions every AI-deploying org should answer
If your org is building AI agents into workflows, ask yourself:
- Do you have an answer quality rubric?
- Is your feedback loop automated, auditable, and actionable?
- Are you seeing hallucination rates drop over time?
If the answer to any of these is "we're working on it," you're flying blind. The agents that compound value are the ones inside a quality system. The ones that don't have a feedback loop degrade silently until someone notices in a bad customer call.
We're helping orgs design systems just like this across sales, data, and ops. Curious what your rubric looks like? Compare notes.