How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?
Agentic benchmarks have emerged across general-purpose and domain-specific settings, including finance, coding, law, and drug discovery, yet energy-domain evaluations remain largely limited to static knowledge recall. This is a critical gap for a sector that requires live data retrieval, specialized regulatory and market knowledge, and multi-step quantitative reasoning under real-world constraints. We present an empirical study of tool-augmented LLM agents on real-world energy market analytics tasks. Our evaluation environment includes 243 expert-curated problems across three categories: (1) Market Data Retrieval and Analysis, (2) Knowledge Retrieval and Interpretation, and (3) Advanced Q...
| Authors | David Akinpelu, Akintonde Abbas, Rereloluwa Alimi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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