EnvOpt: Optimizing Agent Environments Beats Bigger Models on Cost and Pass Rate
AlexGDimakis · x · 2026-11-14
UT Austin professor Alex Dimakis's team introduces Environment Optimization (EnvOpt), studying how the structure of an agent's environment—e.g., whether Slack sits behind an MCP server or is exported as greppable text files—affects agent cost and performance, a lever largely ignored compared to model and harness optimization. Meaningful only for "stationary environments" with large stable artifact collections. On AutomationBench, EnvOpt cuts cost and raises pass rates across all tested agents, outperforming GEPA and Meta-Harness; Claude Haiku 5.5 + EnvOpt beats baseline Claude Opus 5, and EnvOpt stacks with Meta-Harness to push the Pareto frontier.
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