AI Agents Think and Act: ReAct in 2 Minutes
ReAct is a method that enables AI agents to think and act by combining reasoning and action, allowing them to solve complex problems. This approach involves a loop of thought, action, and observation, where the model uses various tools to gather information and update its context. By using ReAct, AI models can provide more accurate answers to complex or real-time questions.
- ▪ReAct turns standard GenAI models into problem solvers by giving them access to various tools like web search, calculators, and databases.
- ▪The ReAct loop consists of thought, action, and observation, where the model plans what to do next, executes an action, and receives output from the tool.
- ▪ReAct is particularly useful when user queries require external information or date-specific references, and can help prevent models from hallucinating or providing incorrect answers.
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| Original publisher | Hacker News (AI / LLM) |
| Canonical URL | https://vinithavn.substack.com/p/how-ai-agents-think-and-act-react |
| Publication time | Thu, 06 Aug 2026 13:49:55 +0000 |
| Retrieval time | 2026-08-06T13:55:47.420Z |
| Last seen | 2026-08-06T13:55:47.420Z |
| 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 | 80vgiX5WzvpA · 2 stories |
| 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 |
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| 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
How AI Agents Think and Act: ReAct in 2 minutesVinitha V NAug 05, 20261ShareImagine asking someone to solve a mystery problem. They just don’t guess the answer at the start or arrive at the answer instantly. They think about what they need, then take an action, look at what they get after the action and then repeat the process until they solve it.That is what ReAct Prompting is, in a nutshell. It is reasoning plus acting.Thanks for reading! Subscribe for free to receive new posts and support my work.SubscribeWhat is ReActStandard GenAI models “guess” (although it is not actual guess) the next word based on the information it already has (ie, through the training process). ReAct turns the model into a problem solver on top of the next word prediction.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
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