Insurance Agent Benchmark: 166 real-world cases for evaluating insurance AI
Doing that has forced us to think deeply about a deceptively simple question: how should we measure whether an AI agent is actually getting better at the work insurance professionals do?Insurance already has useful benchmarks. InsuranceQA evaluates question answering in the insurance domain. INS-MMBench evaluates multimodal understanding and reasoning across insurance scenarios.
- ▪Doing that has forced us to think deeply about a deceptively simple question: how should we measure whether an AI agent is actually getting better at the work insurance professionals do?Insurance already has useful benchmarks.
- ▪InsuranceQA evaluates question answering in the insurance domain.
- ▪INS-MMBench evaluates multimodal understanding and reasoning across insurance scenarios.
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| Original publisher | Cooper |
| Canonical URL | https://www.askcooper.ai/labs/insurance-agent-benchmark |
| Publication time | Thu, 17 Sep 2026 17:44:16 +0000 |
| Retrieval time | 2026-09-17T17:48:44.358Z |
| Last seen | 2026-09-17T17:48:44.358Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | mJG7jAhsOKrV · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
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Opening excerpt (first ~120 words) tap to expand
← Cooper LabsTechnical reportInsurance Agent Benchmark (IAB)Measuring how AI agents handle the day-to-day work of insurance professionals.Phase one · documents166 documents42 document kinds11 test tracks17 models × 2 modeshuman-verified ground truthIshika Shah·Founding Engineer·September 16, 2026On this pageOn this page01Why we built an insurance agent benchmark02Key Findings03How does the harness impact model performance?04How we built IAB05Results in detail06Where AI still breaks07How we judge the answers08Scope and limitations09From documents to the whole job10Citation§1Why we built an insurance agent benchmarkAt Cooper, we're building an AI coworker for insurance.
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