Story
The agent harness belongs outside the sandbox
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Overview
An agent harness drives a large language model (LLM) by sending prompts, receiving responses, executing tool calls, and iterating until completion. The location of the harness influences security, failure modes, and the capabilities of the agent, with distinct tradeoffs for single-user agents.
Summary from mendral.com.
Reports
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Community reactions
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Hacker News · mendral.com
Hacker News discussion ↗
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Based on 5 sampled comments from this thread. Analyzed 2026-05-03.
- Praise: The rapid evolution of AI models necessitates flexible architectures, as seen in the frequent redesigns of harnesses to keep pace with advancements.
- Praise: The idea of agents driving their own workflows reflects a significant leap in AI capabilities, moving beyond simple task execution to more autonomous decision-making.
- Praise: Exploring different architectures for agent harnessing can lead to innovative solutions that enhance the performance and safety of AI applications.
- Concern: There are significant trust issues surrounding the harnesses, as many users feel they cannot rely on them to provide adequate safety constraints against LLM exploits.
- Concern: The lack of clarity in security models when harnesses operate outside sandboxes raises concerns about potential vulnerabilities and misuse of AI capabilities.
- Concern: The rapid changes in AI models make existing architectures obsolete quickly, creating a chaotic environment for developers trying to implement stable solutions.