AutoOnboarder: Autonomously Onboarding LLM Agents

Tanay Arora; Cong Liu

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arXiv · 2610.21218

AutoOnboarder: Autonomously Onboarding LLM Agents

Tanay Arora·Cong Liu

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Abstract

Agentic large language models have been documented to lose reasoning capability when placed inside a harness, the software that mediates their observations and actions. To reconcile these losses, we introduce AutoOnboarder, an autonomous system that adapts an agent to a task the way a trainer onboards a new employee. It places the agent under the supervision of an intelligent ``Onboarder'' coding agent, which iteratively tailors its foundation model, prompt, and harness source code to simultaneously improve performance and decrease cost. Like a real onboarding process, the Onboarder is only given tens of examples, and all evaluation data is isolated on a separate virtual machine to prevent leakage. We report the lessons learned from building and testing AutoOnboarder on genomic-sequencing prior authorization form submission. Autonomously onboarded agents are able to match or exceed the performance of a Gemini 3.5 Flash baseline at up to 29$\times$ lower costs. However, these agents remain imperfect, and the Onboarding process varies greatly in terms of final performance. We conclude that future Onboarding systems require a robust environment for the Onboarder, including a structured thought interface, (controlled) internet access, and a wide model search space.

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Tanay Arora, Cong Liu. 2026-10-15. AutoOnboarder: Autonomously Onboarding LLM Agents. https://arxiv.org/abs/2610.21218

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