How Much Heavy Lifting Can an Agent Harness Do?: Measuring the LLM's Residual Role in a Planning Agent
Agent harnesses -- the stateful programs that wrap a language model and decide what it sees at each step -- are now known to change end-to-end performance on a fixed model by as much as six times. That raises a question asked less often than it should be: how much of an agent's competence does the harness itself already carry, and how much genuinely still needs the LLM? We externalize a planning harness for noisy Collaborative Battleship into four progressively richer layers -- posterior belief tracking, declarative planning, symbolic reflec tion, and an LLM-backed revision gate -- under a common runtime, taking \emph{win rate} as the primary metric and \emph{F1} as secondary, and pre-spe...
| Authors | Sungwoo Jung, Seonil Son |
|---|---|
| Primary category | cs.AI |
| Published | 8 April 2026 |
| Last updated at source | 28 April 2026 |
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