Agents Training Agents: A practical architecture for autonomous self-improvement
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Re: Agents Training Agents: A practical architecture for autonomous self-improvement
#2I get the impression the anonymous author has not tried the things he is writing about. Otherwise I'd like to see his results; e.g., for the "How Does the Agent Know What It Doesn’t Know?" section.
Re: Agents Training Agents: A practical architecture for autonomous self-improvement
#3I get the impression the anonymous author has not tried the things he is writing about. Otherwise I'd like to see his results; e.g., for the "How Does the Agent Know What It Doesn’t Know?" section.
When we set out to implement this within an n8n automation, we encountered some implementation challenges. The issues stemmed from the self-training process—specifically regarding the interest and stress score tree—reaching information saturation, which led to a decay in curiosity. However, keeping the threshold constant (not updating it) fundamentally resolved the issue. To be clear, the experiment has only just begun; this is merely an introductory post outlining the basic architecture.
Re: Agents Training Agents: A practical architecture for autonomous self-improvement
#4I get the impression the anonymous author has not tried the things he is writing about. Otherwise I'd like to see his results; e.g., for the "How Does the Agent Know What It Doesn’t Know?" section.
When we set out to implement this within an n8n automation, we encountered some implementation challenges. The issues stemmed from the self-training process—specifically regarding the interest and stress score tree—reaching information saturation, which led to a decay in curiosity. However, keeping the threshold constant (not updating it) fundamentally resolved the issue. To be clear, the experiment has only just beg…
In that case I would start by studying the literature. The first two uncertainty estimation & out-of-distribution (OOD) detection approaches you mention, "Embedding Distance" and "Self-Interrogation", are sometimes called feature-space density estimation and consistency-based uncertainty quantification. Practical algorithms include Semantic Entropy, Self-Consistency / Verbalized Confidence, and Embedding-based Density (Mahalanobis Distance).
References:
A Survey of Uncertainty Estimation Methods on Large Language Models (https://aclanthology.org/2025.findings-acl.1101/)
A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice (https://arxiv.org/abs/2410.15326v1)