2023
Reflexion: Language Agents with Verbal Reinforcement Learning
Cheap, and it works. When an agent fails a task, a written critique plus one retry often beats a bigger model.
Generalises chain-of-thought into a search tree. The model proposes multiple partial solutions, evaluates them against each other, and can back out of a branch that is not working, rather than committing to one linear line of reasoning.
@inproceedings{yao2023tree,
author = {Shunyu Yao and Dian Yu and Jeffrey Zhao and Izhak Shafran and Thomas L. Griffiths and Yuan Cao and Karthik Narasimhan},
title = {Tree of Thoughts: Deliberate Problem Solving with Large Language Models},
booktitle = {Advances in Neural Information Processing Systems 36 (NeurIPS 2023)},
year = {2023},
eprint = {2305.10601},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2305.10601}
}Ejentic did not author this paper. Credit belongs to the authors named above; the note is ours.
2023
Cheap, and it works. When an agent fails a task, a written critique plus one retry often beats a bigger model.
2022
The pattern behind most of the agents we build. The trace is not decoration: it is the only way to debug an agent that took a wrong turn three steps ago.
2026
Read this before you let an agent call anything a customer will see the output of. The failure mode is not a crash, it is an API returning 200 with a body that means "I did not understand you". There is no exception to catch and no status code to branch on. The result we quote most often is the last one: the fix was one line of schema, not a better model.