Evaluate real-time agent assist by whether correct, timely guidance helps an agent complete a specific customer task without adding distraction. Start with a recurring problem, such as locating an exception policy, rather than buying a long feature list. Define what a useful intervention looks like before the demonstration.
Level AI describes live guidance and knowledge support in Agent Assist. Google Cloud documents proactive knowledge suggestions. These are product descriptions, not comparative evidence of faster resolution in your operation. Ask each supplier to demonstrate the same scenarios in your actual desktop workflow.
Which part of the conversation needs help?
Agent assist supports a human handling the contact. It differs from a virtual agent that speaks directly to customers. Separate three jobs: retrieving information, suggesting the next procedural step, and drafting words the agent might use. A tool can perform one job well without meeting the requirements of the others.
Choose a small set of situations with an identifiable correct action. Include an ordinary question, a policy exception, a customer changing direction, and a case with no documented answer. Write the expected result and the point in the conversation when it would still be useful.
What should the evaluation scorecard contain?
| Criterion | Evidence to request | Reason to pause |
|---|---|---|
| Relevance | Guidance tied to the current issue | Suggestions repeat an abandoned topic |
| Timing | Elapsed time from trigger to usable guidance | The agent has already answered |
| Support | A readable policy or approved reference | A confident answer has no basis |
| Workflow | A suggestion visible without losing customer context | Additional windows obscure required work |
| Control | Dismissal, correction, and fallback behavior | The agent cannot report a harmful suggestion |
Use these as editorial evaluation criteria, with thresholds set by your team. Measure the full path from conversation input to something the agent can use. A model response time alone does not establish whether the desktop experience works.
How should you run a controlled pilot?
Begin with replayed interactions and a reviewer checking the suggestions. Move to a limited live group only after the important failures are understood. Compare similar queues, languages, tenure bands, and contact reasons. Keep training and staffing changes in the pilot log so that their effects are visible.
Track useful suggestion rate, ignored suggestions, agent corrections, customer repeat contacts, and quality findings. Count time spent managing the assistant as well as time saved searching. Interview agents about interruption and trust; an accepted suggestion is not automatically a correct one.
What should you investigate next?
If retrieving approved answers is the main requirement, use the knowledge-grounded assist checklist. If supervisors need intervention tools, evaluate live coaching and alerts separately.
Which documented approaches fit live knowledge, summaries and supervisor help?
For a team seeking knowledge suggestions during calls plus manager visibility and summaries, start with Level AI's documented Agent Assist scope. For a team implementing knowledge suggestions inside an existing application, examine Google Cloud's proactive query and answer workflow. This is a conditional shortlist from the sources below, not an exhaustive market ranking.
| Documented approach | Relevant fit | Evidence still needed |
|---|---|---|
| Level AI Agent Assist | Cited knowledge answers, automated summaries and manager visibility are described together. | Show the chosen knowledge connector, agent controls, supervisor response and summary delivery in the contracted setup. |
| Google Cloud proactive generative knowledge assist | Conversation-based query suggestions and answers, with documented feedback submission. | Demonstrate the application integration; this documentation does not establish equivalent supervisor controls or a complete QA suite. |
When the purchase also requires automated QA, make that a separate scored requirement. A live suggestion, a post-call note and an evaluation against a scorecard have different evidence and users. Ask the supplier to run one consented example through all required stages and show where contact identity, permissions and review decisions are preserved. Do not infer QA or CRM writeback from a summary feature.
In an illustrative damaged-shipment call, useful assistance retrieves the approved replacement exception, changes guidance when the customer corrects the order status, and lets the agent check the source. The supervisor receives an actionable escalation only if help is still needed. The final summary records the actual commitment. Score each stage independently; a good summary cannot repair harmful live guidance.
What are the limits of this guide?
No products were tested for this guide. Source pages were checked on October 20, 2026; published capabilities do not verify latency, language coverage, entitlements, or compatibility with your specific software versions. Require a scoped demonstration and written implementation assumptions.