--- title: AI Agents Explained: How to Create and Use AI Agents in 2026 description: AI agents differ from chatbots by combining an LLM with tools, memory, and goals in an observe-think-act loop. Four agent platforms are compared: Claude Code... canonical: https://daily.dev/posts/ai-agents-explained-how-to-create-and-use-ai-agents-in-2026-10o6po7fl twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: AI Agents Explained: How to Create and Use AI Agents in 2026 | daily.dev og:description: AI agents differ from chatbots by combining an LLM with tools, memory, and goals in an observe-think-act loop. Four agent platforms are compared: Claude Code... og:url: https://daily.dev/posts/ai-agents-explained-how-to-create-and-use-ai-agents-in-2026-10o6po7fl og:image: https://api.daily.dev/og/posts/10O6PO7fL.png og:image:alt: AI Agents Explained: How to Create and Use AI Agents in 2026 og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # AI Agents Explained: How to Create and Use AI Agents in 2026 **[YouTube](https://daily.dev/sources/youtube)** · 24 min read · 0 upvotes · 0 comments ## Summary AI agents differ from chatbots by combining an LLM with tools, memory, and goals in an observe-think-act loop. Four agent platforms are compared: Claude Code (best for reasoning and complex orchestration), Codex (lowest friction for ChatGPT subscribers), OpenClaw (life automation via messaging apps), and Antigravity (visual/frontend work). A key framework called the 'prompt contract' is introduced with four sections — goal, constraints, format, and failure — to prevent agents from going off-rails. Additionally, using a persistent memory file (claude.md, agents.md, etc.) lets agents accumulate rules across sessions, reducing repeated mistakes. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [AI Agents Explained](https://daily.dev/posts/ai-agents-explained-9hh27gy7n) · Trevor Lasn · 2 upvotes · 0 comments - [Why and how to use AI agents in product design: a practical ChatGPT tutorial](https://daily.dev/posts/why-and-how-to-use-ai-agents-in-product-design-a-practical-chatgpt-tutorial-jdxwauaqs) · UX Planet · 1 upvotes · 1 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#automation](https://daily.dev/tags/automation), [#ai-agents](https://daily.dev/tags/ai-agents), [#prompt-engineering](https://daily.dev/tags/prompt-engineering), [#claude-code](https://daily.dev/tags/claude-code) [View this post on daily.dev](https://daily.dev/posts/ai-agents-explained-how-to-create-and-use-ai-agents-in-2026-10o6po7fl) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"AI Agents Explained: How to Create and Use AI Agents in 2026","url":"https://daily.dev/posts/ai-agents-explained-how-to-create-and-use-ai-agents-in-2026-10o6po7fl","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/ai-agents-explained-how-to-create-and-use-ai-agents-in-2026-10o6po7fl"},"datePublished":"2026-06-07T04:48:10.501Z","dateModified":"2026-06-07T04:48:31.005Z","description":"AI agents differ from chatbots by combining an LLM with tools, memory, and goals in an observe-think-act loop. 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