SAAG: Structured Agent Assessment and Grounding
Exact-match evaluation of agent-calling obscures qualitatively different failure modes: a model may select the right function yet hallucinate argument values, or satisfy a schema while choosing a agent for the wrong reason. Existing benchmarks collapse these distinctions into a single binary score, leaving practitioners unable to diagnose where agent calls fail. We propose SAAG a cascaded diagnostic framework that decomposes agent-calling evaluation into three sequential stages: registry conformance, structural completeness, and argument grounding, each producing interpretable stage-specific diagnostics. These diagnostics additionally enable iterative self-repair: on prediction failure, t...
| Authors | Ritvik Garimella, Vedant Khandelwal, Anvi Kohli et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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