Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuel
Across many domains (e.g., IoT, observability, telecommunications, cybersecurity), there is an emerging adoption of conversational data analysis agents that enable users to "talk to your data" to extract insights. Such data analysis agents operate on timeseries data models; e.g., measurements from sensors or events monitoring user clicks and actions in product analytics. We evaluate 6 popular data analysis agents (both open-source and proprietary) on domain-specific data and query types, and find that they fail on stateful and incident-specific queries. We observe two key expressivity gaps in existing evals: domain-customized datasets and domain-specific query types. To enable practitione...
| Authors | Aadyaa Maddi, Prakhar Naval, Deepti Mande et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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