Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuel
This addresses the need for better evaluation tools for timeseries data analysis agents in domains like IoT and cybersecurity, though it is incremental as it builds on existing agent frameworks.
The paper tackled the problem of evaluating conversational data analysis agents on timeseries data, finding that existing agents fail on stateful and incident-specific queries, and introduced AgentFuel to generate customizable evals, showing it exposes improvement directions and anecdotal performance gains.
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 practitioners in such domains to generate customized and expressive evals for such timeseries data agents, we present AgentFuel. AgentFuel helps domain experts quickly create customized evals to perform end-to-end functional tests. We show that AgentFuel's benchmarks expose key directions for improvement in existing data agent frameworks. We also present anecdotal evidence that using AgentFuel can improve agent performance (e.g., with GEPA). AgentFuel benchmarks are available at https://huggingface.co/datasets/RockfishData/TimeSeriesAgentEvals.