Cost-Aware AI Experiment Orchestrator for Research
C6/10March 23, 2026
WhatA layer on top of LLM coding agents that estimates computational cost of each proposed experiment, ranks proposals by expected improvement per dollar, and presents a prioritized list for human approval before execution.
SignalMultiple practitioners express frustration that LLM agents will happily burn through enormous token and compute budgets trying every suggestion, most of which are low-value hyperparameter tweaks — they want the exploration but with economic discipline and human oversight on what actually gets run.
Why NowCoding agents like Claude Code are now powerful enough that people actually let them run unsupervised experiments, but the cost of unconstrained agent loops is becoming painfully visible as usage scales.
MarketML teams and individual researchers using coding agents for experimentation; adjacent to MLOps market (~$4B); key gap is that neither Optuna/Vizier (no LLM reasoning) nor raw coding agents (no cost awareness) solve this well.
MoatHistorical data on cost-vs-improvement across experiment types builds a pricing/value model that gets better with scale, creating a data moat around experiment prioritization.
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