Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuel

2026-03-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

AgentFuel is a new framework designed to help domain experts create customized and expressive evaluations (evals) for conversational data analysis agents that operate on timeseries data. An evaluation of six popular open-source and proprietary data analysis agents revealed significant failures on stateful and incident-specific queries, highlighting expressivity gaps in existing evaluation methods, particularly concerning domain-customized datasets and domain-specific query types. AgentFuel addresses these gaps by enabling practitioners in domains like IoT, observability, telecommunications, and cybersecurity to generate end-to-end functional tests. Benchmarks created with AgentFuel expose critical areas for improvement in current data agent frameworks and have shown anecdotal evidence of improving agent performance, such as with GEPA. The AgentFuel benchmarks are publicly available on Hugging Face.

Key takeaway

For AI Architects evaluating conversational data analysis agents for timeseries data, you should integrate AgentFuel into your testing pipeline. This will allow you to create more expressive, domain-specific evaluations that uncover critical performance gaps, especially for stateful and incident-specific queries, leading to more robust agent deployments.

Key insights

Existing timeseries data analysis agents fail on stateful and incident-specific queries due to evaluation expressivity gaps.

Principles

• Domain-specific evals are crucial for timeseries agents.
• Customized datasets improve agent evaluation.

Method

AgentFuel allows domain experts to quickly create customized evals for end-to-end functional testing of timeseries data analysis agents, addressing gaps in domain-specific datasets and query types.

In practice

• Use AgentFuel to test timeseries agents.
• Create domain-customized datasets for evals.

Topics

• Data Analysis Agents
• Timeseries Data Analysis
• Agent Evaluation
• Benchmarking
• AgentFuel

Best for: AI Architect, AI Scientist, Research Scientist, AI Researcher, AI Engineer, Machine Learning Engineer

Related on AIssential

• Do More Agents Help? Controlled and Protocol-Aligned Evaluation of LLM Agent Workflows — BenchAgent reveals multi-agent LLM workflows often underperform single-agent setups under controlled conditions, except for specific runtime-generated designs.
• This Is How Marketers Can Use AI Agents for Data Analysis — AI agents can autonomously conduct multi-step data investigations from a high-level objective, functioning as a delegated analyst.
• EVA-Bench Data 2.0: 3 Domains, 121 Tools, 213 Scenarios — Reproducible voice agent evaluation requires domain-specific, realistic, and varied scenarios with robust, jointly generated ground truth.
• Presentation: AI Agents to Make Sense of Data at OpenAI — OpenAI's Kepler agent uses advanced AI techniques to automate complex data analysis across vast datasets, improving data accessibility and accuracy.
• The Sequence Opinion #806: The Emergence of the Agent-as-a-Judge: Why Evals Need a Reasoning Engine — AI evaluation is evolving from simple LLM-as-a-judge to agent-as-a-judge with reasoning capabilities.
• Democratizing business intelligence: BGL’s journey with Claude Agent SDK and Amazon Bedrock AgentCore — A robust data foundation and modular AI agent design are crucial for secure, scalable, and accurate natural language data analysis.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.