This is a delivery-side operator brief. The important question is not whether the capability exists. The question is whether the workflow can carry that capability into production with a named owner, measurable quality, and a stable handoff model.
Challenge
Many support teams start by judging AI quality through tone, fluency, or customer-like feel. That misses the parts that actually determine operational success.
What Changed
- Realtime and agentic support systems can now do more than answer questions—they can route, summarize, suggest actions, and gather evidence.
- As capability expands, the eval target must expand too.
- Teams need workflow scoring, not just conversational scoring.
Outcomes
- A more accurate view of whether the system is helping support operations
- Better iteration priorities because routing and evidence errors become visible
- Stronger trust in rollout decisions
Why it worked / Next step
Resolution quality is path quality. Measure whether the agent used the right knowledge, chose the right next step, escalated at the right time, and preserved context cleanly.
Related engagement model: Improvement
Supporting solutions: Quality, Operations
Relevant service building blocks: evaluations (evals) and quality assurance; measurement framework and success criteria; human-in-the-loop design; monitoring and maintenance plan
If this is close to the blocker inside your team, the practical next step is to scope one workflow, define the operating boundary, and ship the first controlled release with review gates and ownership already in place.