{"id":59,"date":"2025-07-30T19:36:59","date_gmt":"2025-07-30T19:36:59","guid":{"rendered":"https:\/\/cs.tarabitab.com\/ing\/?p=59"},"modified":"2025-07-30T19:38:04","modified_gmt":"2025-07-30T19:38:04","slug":"about-ai-agents","status":"publish","type":"post","link":"https:\/\/cs.tarabitab.com\/ing\/2025\/07\/30\/about-ai-agents\/","title":{"rendered":"About AI Agents"},"content":{"rendered":"\n

This is a good starting point if you’re trying to make sense of AI agents.<\/p>\n\n\n\n

There\u2019s a lot of talk about agent frameworks right now, but at the core, most of them build on the same set of ideas.<\/p>\n\n\n\n

This cheat sheet gives a simple overview of the key building blocks, from LLMs to orchestration to protocols. Useful whether you\u2019re exploring agent tooling, building internal automations, or just trying to understand the space better.<\/p>\n\n\n\n

What is an AI agent?
– Agents combine reasoning with the ability to take action.
– They don\u2019t just respond, they can plan, call tools, access data, and trigger real-world effects.<\/p>\n\n\n\n

Language Model
– This is the core reasoning engine. It interprets input and generates plans or responses.
– But by itself, it can\u2019t take real-world actions.<\/p>\n\n\n\n

Tools
– APIs, functions, and external integrations that agents use to do useful work, like querying a database, sending an email, or calling a webhook.<\/p>\n\n\n\n

Orchestration Layer
– This layer coordinates what the agent does, how it reasons (via CoT, ReAct, etc.), how it sequences steps, and how it interacts with tools.<\/p>\n\n\n\n

Agentic Protocols
– Protocols like MCP and A2A enable agents to collaborate across platforms (e.g., Slack, GitHub) and maintain context across tasks.<\/p>\n\n\n\n

Building AI Agents
– There\u2019s no single way to build an agent.
– Some start with a single prompt. Others use low-code platforms. Some teams build full custom frameworks.<\/p>\n\n\n\n

The cheat sheet maps out the trade-offs.<\/p>\n\n\n\n

If you\u2019re trying to understand the agent space, or explain it to your team, this breakdown might help<\/p>\n\n\n\n

\"\"<\/figure>\n","protected":false},"excerpt":{"rendered":"

This is a good starting point if you’re trying to make sense of AI agents. There\u2019s a lot of talk about agent frameworks right now, but at the core, most of them build on the same set of ideas. This cheat sheet gives a simple overview of the key building blocks, from LLMs to orchestration … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1,3,2],"tags":[],"class_list":["post-59","post","type-post","status-publish","format-standard","hentry","category-chatgpt","category-deepseek","category-grok"],"_links":{"self":[{"href":"https:\/\/cs.tarabitab.com\/ing\/wp-json\/wp\/v2\/posts\/59","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cs.tarabitab.com\/ing\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cs.tarabitab.com\/ing\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cs.tarabitab.com\/ing\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/cs.tarabitab.com\/ing\/wp-json\/wp\/v2\/comments?post=59"}],"version-history":[{"count":2,"href":"https:\/\/cs.tarabitab.com\/ing\/wp-json\/wp\/v2\/posts\/59\/revisions"}],"predecessor-version":[{"id":62,"href":"https:\/\/cs.tarabitab.com\/ing\/wp-json\/wp\/v2\/posts\/59\/revisions\/62"}],"wp:attachment":[{"href":"https:\/\/cs.tarabitab.com\/ing\/wp-json\/wp\/v2\/media?parent=59"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cs.tarabitab.com\/ing\/wp-json\/wp\/v2\/categories?post=59"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cs.tarabitab.com\/ing\/wp-json\/wp\/v2\/tags?post=59"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}