What's inside an AI agent: a 300~ LoC ReAct loop
The article explores the inner workings of a simplified AI agent built using a 300-line ReAct loop. It highlights the potential risks associated with Actions, which can lead to unintended consequences if not managed properly. The author emphasizes the importance of context management and encourages software engineers to create custom agents tailored to their specific needs.
- ▪The AI agent is built using a 300-line ReAct loop, which can be error-prone when using a small local model.
- ▪Actions in the AI agent can lead to significant risks, such as executing harmful commands if not carefully controlled.
- ▪Context management is crucial, as every step in the loop resends the entire history, impacting performance and costs.
Opening excerpt (first ~120 words) tap to expand
What's actually inside an AI agent: a 300~ LoC ReAct loop Published on I wanted to build my own simplification of an AI Agent — to see past the hype, and to figure out what changes when our applications start running one. I had pieces scattered around but never sat down with them. First, I sketched it in pseudo-code, read a few of the papers that shaped what's now in production, chatted with mainstream agents to fill the gaps, and ended up with this: It's simple, and also a bit error-prone because I'm running a small local model instead of a Frontier one. It answers incorrectly sometimes, and a single bad step poisons the whole chain. The point I really want to land, though, is how Actions can be anything — and how risky that is once you stop treating it as a demo.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Quantumentangled.
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