The Tool Desk
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How an AI support-agent evaluation works
A useful evaluation resembles a controlled customer-service environment: the agent receives customer context, support policies, relevant knowledge, and access to the tools it is expected to use. It then handles a shared set of realistic cases while evaluators capture what it said, what it did, and what changed in the system.
- Define the job and success conditions. Choose support intents and edge cases, then specify what counts as success, partial success, or failure. Set policy boundaries, allowed actions, required checks, and cases that must be escalated before scoring begins.
- Build a controlled environment. Provide representative customer and account data, written policies, relevant knowledge, and working tools such as refund, subscription, or account-update actions. For one example of a published setup, G2’s current Customer Experience methodology uses a simulated company, a written policy, and 38 working business tools. Read G2’s methodology.
- Run the same realistic tasks across agents. Include multi-turn conversations, ambiguous requests, policy exceptions, and situations where the right action is to ask a clarifying question or hand off to a person. G2 says each evaluated CX agent completes 46 buyer-informed support tasks drawn from buyer research, design partners, and synthetic edge cases; that is an attribute of its benchmark, not a universal requirement.
- Capture the full trace and final outcome. Record the conversation, relevant context, tools selected, arguments passed, tool responses, escalation decisions, and final system state. G2’s scoring explanation says its evaluation considers the full conversation, observable tool calls, and the simulated environment’s end state. See G2’s scoring explanation.
- Score what happened and how it happened. Use deterministic checks for observable events and final-state requirements, alongside rubrics for qualities such as relevance, completeness, and policy interpretation. Publish the criteria, denominator, and any weighting so results can be interpreted and reproduced.
- Inspect failures, improve, and rerun. Group errors by cause, change the agent or workflow, then retest on held-out or refreshed cases. Repeated runs matter because a single successful answer does not establish consistent behavior.
- Validate finalists in your own environment. Public benchmarks can help narrow choices, but a finalist still needs testing against your organization’s policies, integrations, approval rules, and cost model. G2 explicitly recommends local validation.
What a thorough evaluation should measure
Resolution is only one dimension. An agent can produce a polished response while taking an unauthorized action, skipping a required verification, or claiming that an action succeeded when it did not. Evaluate the customer outcome and the path to it.
| Dimension | Questions to ask | Example measures |
|---|---|---|
| Outcome | Was the customer’s need resolved correctly? | Task success, resolution rate, final-state correctness, answer quality |
| Policy and safety | Did the agent follow policy and avoid prohibited actions? | Policy adherence, unsafe-action rate, sensitive-data handling, authorization correctness |
| Tool trajectory | Did it choose tools and arguments correctly, follow required steps, and verify results? | Tool-call success, argument correctness, required-step completion, recovery after tool errors |
| Escalation | Did it hand off cases requiring a person while resolving cases it was authorized to handle? | Escalation precision or calibration, unnecessary escalation, missed escalation |
| Grounding and knowledge | Were answers supported by relevant policy or knowledge? | Groundedness, retrieval relevance, unsupported-claim rate, knowledge-source use |
| Customer outcome over time | Was the interaction useful, and did it avoid preventable repeat contact? | First-contact resolution, satisfaction, repeat-contact rate |
| Operations and consistency | Is performance practical and repeatable? | Latency, cost per task, retries, tool-call volume, pass rate across repeated runs |
Microsoft’s Copilot Studio metric reference defines measures including resolution, escalation, deflection, first-contact resolution, autonomous tool use, knowledge-source use, generated answer quality, and groundedness. It defines first-contact resolution using a seven-day window: the issue is resolved on the first interaction without a return contact within seven days. The definition and observation window matter when comparing results. Consult Microsoft’s agent metrics reference.
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Snowflake groups agent metrics into outcome, trajectory, reasoning, safety and compliance, operations, and consistency. Together, these frameworks point to a practical rule: report meaningful dimensions separately where possible. One composite score can hide trade-offs, such as strong resolution paired with unsafe actions or low cost paired with skipped verification. Read Snowflake’s evaluation overview.
Why the conversation alone is not enough
Support agents can fail in ways that are invisible in the final wording. They might answer before checking the customer record, escalate a case they could have resolved, or take the wrong action and report success. G2 describes these kinds of failures in its account of evaluating AI customer-service agents. Read G2’s evaluation findings.
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For that reason, score the agent’s tool trajectory and the resulting account or ticket state as well as its response. Check whether it verified identity where required, selected the permitted action, supplied correct tool arguments, interpreted the tool’s result, and accurately described what happened. A fluent answer is not evidence that the underlying task was completed correctly.
How to compare AI support agents fairly
When comparing two or more systems, hold the task set, policies, data, tool access, and scoring rubric constant. Report the dimensions independently so a strength in one area does not conceal a serious weakness elsewhere.
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- Resolution quality: correctness and completeness of customer outcomes.
- Policy and safety: permission handling, prohibited actions, and required escalations.
- Tool reliability: tool selection, arguments, result interpretation, and verification.
- Consistency: performance across repeated runs rather than one favorable sample.
- Customer experience: clarity, relevance, appropriate clarification, and satisfaction.
- Operating fit: latency, total cost per resolved task, retry burden, and auditability.
Keep the benchmark’s task mix, product configuration, policies, evaluator, and methodology version alongside any result. A benchmark is evidence about the tested agents on those tasks under those conditions—not a promise of performance on a different company’s workflows. G2 describes its evaluation as a dated snapshot and says it plans quarterly refreshes. Controlled benchmark results should also remain distinct from customer review ratings and vendor-reported claims.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What published examples can—and cannot—tell you
G2’s current published Customer Experience methodology uses 46 buyer-informed support tasks in a simulated company with 38 working tools. Its scoring explanation says the first CX run covered 10 agents and roughly 700 recorded conversations. These numbers describe G2’s particular benchmark setup and first run; they do not establish a universal minimum sample size or tool count.
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A 2026 preprint, Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework, reports a card-delivery deployment A/B test in which the authors attribute a 37 percentage-point improvement in AI transactional Net Promoter Score and a 29 percentage-point gain in self-service rate to agent variants. Those deployment-specific findings should not be treated as expected results for other organizations or proof that an offline benchmark predicts every production setting. Read the preprint.
There is no universally accepted single score, required number of cases, or universal pass threshold for AI support-agent evaluations. Deflection also should not be treated as synonymous with a solved problem: Microsoft defines it as self-service resolution rather than escalation, so the event and denominator should be made explicit when reporting it.
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