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.
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Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Quantumentangled |
| Canonical URL | https://quantumentangled.dev/viewpost/11/whats-actually-inside-an-ai-agent-a-300-loc-react-loop |
| Publication time | Mon, 18 May 2026 00:34:16 +0000 |
| Retrieval time | 2026-05-18T00:38:21.130Z |
| Last seen | 2026-05-18T00:38:21.130Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | dlP54k1icN7f |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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