AgentFuel: Custom Evals for Timeseries Agents
๐กNew tool exposes gaps in top timeseries AI agents + free benchmarks
โก 30-Second TL;DR
What Changed
Evaluated 6 agents (open/proprietary) failing on stateful/incident timeseries queries
Why It Matters
AgentFuel fills expressivity gaps in evals, aiding IoT/cybersecurity practitioners to benchmark and refine timeseries agents effectively. It highlights weaknesses in popular frameworks, driving targeted improvements.
What To Do Next
Download AgentFuel benchmarks from Hugging Face and eval your timeseries agent.
Key Points
- โขEvaluated 6 agents (open/proprietary) failing on stateful/incident timeseries queries
- โขAgentFuel enables quick custom evals for domain-specific timeseries data
- โขExposes improvement areas in frameworks; boosts performance e.g., GEPA
- โขBenchmarks at huggingface.co/datasets/RockfishData/TimeSeriesAgentEvals
๐ง Deep Insight
Background and context from public sources โ not the original article. 8 sources cited.
๐ Enhanced Key Takeaways
- โขAgentFuel was developed by researchers from Rockfish Data and Carnegie Mellon University, including lead author Aadyaa Maddi.[2]
- โขThe paper was submitted to arXiv on March 12, 2026, as version v1, focusing on domains like IoT, observability, telecommunications, and cybersecurity.[1]
- โขAgentFuel uses domain-customized datasets and incident-specific query types to reveal expressivity gaps not captured by general benchmarks.[1]
๐ฎ Future ImplicationsAI analysis grounded in cited sources
โณ Timeline
๐ Sources (8)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
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Original source: ArXiv AI โ
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