{"id":4725,"date":"2026-01-28T08:33:55","date_gmt":"2026-01-28T08:33:55","guid":{"rendered":"https:\/\/taoailab.com\/?p=4725"},"modified":"2026-01-28T08:33:55","modified_gmt":"2026-01-28T08:33:55","slug":"yapay-zeka-ajanlarinin-kalbine-yolculuk-codex-agent-loop-nasil-calisir","status":"publish","type":"post","link":"https:\/\/taoailab.com\/en\/yapay-zeka-ajanlarinin-kalbine-yolculuk-codex-agent-loop-nasil-calisir\/","title":{"rendered":"A Journey into the Heart of AI Agents: How the Codex \u201cAgent Loop\u201d Works?"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"4725\" class=\"elementor elementor-4725\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a09f237 e-flex e-con-boxed tcg-animation-none e-con e-parent\" data-id=\"a09f237\" data-element_type=\"container\" data-settings=\"{&quot;tc_container_hover_selector&quot;:&quot;container&quot;,&quot;tc_container_background_parallax&quot;:&quot;no&quot;,&quot;tc_smooth_scroll_effects&quot;:&quot;none&quot;,&quot;tc_css_effects&quot;:&quot;none&quot;,&quot;tc_container_clip_path&quot;:&quot;none&quot;,&quot;tcg_advanced_hover&quot;:&quot;no&quot;,&quot;float_cursor&quot;:&quot;no&quot;,&quot;tc_dark_mode_responsive_hide_in_dark&quot;:&quot;no&quot;,&quot;tc_dark_mode_responsive_hide_in_light&quot;:&quot;no&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1d3d8e5 tcg-animation-none elementor-widget elementor-widget-image\" data-id=\"1d3d8e5\" data-element_type=\"widget\" data-settings=\"{&quot;tc_smooth_scroll_effects&quot;:&quot;none&quot;,&quot;tc_css_effects&quot;:&quot;none&quot;,&quot;tc_dark_mode_responsive_hide_in_dark&quot;:&quot;no&quot;,&quot;tc_dark_mode_responsive_hide_in_light&quot;:&quot;no&quot;}\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"559\" src=\"https:\/\/taoailab.com\/wp-content\/uploads\/2026\/01\/Gemini_Generated_Image_5beivb5beivb5bei-1024x559.png\" class=\"attachment-large size-large wp-image-4727\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1e9d338 e-flex e-con-boxed tcg-animation-none e-con e-parent\" data-id=\"1e9d338\" data-element_type=\"container\" data-settings=\"{&quot;tc_container_hover_selector&quot;:&quot;container&quot;,&quot;tc_container_background_parallax&quot;:&quot;no&quot;,&quot;tc_smooth_scroll_effects&quot;:&quot;none&quot;,&quot;tc_css_effects&quot;:&quot;none&quot;,&quot;tc_container_clip_path&quot;:&quot;none&quot;,&quot;tcg_advanced_hover&quot;:&quot;no&quot;,&quot;float_cursor&quot;:&quot;no&quot;,&quot;tc_dark_mode_responsive_hide_in_dark&quot;:&quot;no&quot;,&quot;tc_dark_mode_responsive_hide_in_light&quot;:&quot;no&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5847c91 tcg-animation-none elementor-widget elementor-widget-text-editor\" data-id=\"5847c91\" data-element_type=\"widget\" data-settings=\"{&quot;tc_smooth_scroll_effects&quot;:&quot;none&quot;,&quot;tc_css_effects&quot;:&quot;none&quot;,&quot;tc_dark_mode_responsive_hide_in_dark&quot;:&quot;no&quot;,&quot;tc_dark_mode_responsive_hide_in_light&quot;:&quot;no&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h1 data-start=\"151\" data-end=\"217\">A Journey into the Heart of AI Agents: How the Codex \u201cAgent Loop\u201d Works?<\/h1><p data-path-to-node=\"6\">In the software world, AI agents are no longer just assistants; they have become an integral part of our workflows. Today, we\u2019re going \u201cunder the hood\u201d of OpenAI\u2019s cross-platform local software agent, <b data-path-to-node=\"6\" data-index-in-node=\"165\">Codex CLI<\/b>to explore the <b data-path-to-node=\"6\" data-index-in-node=\"227\">Agent Loop<\/b> -the core logic that serves as the system\u2019s \u201cbrain.\u201d<\/p><p data-path-to-node=\"7\">How does an AI agent understand your command and perform meaningful software work like creating actual files on your machine? Here is the step-by-step breakdown of the Codex design:<\/p><h4 data-path-to-node=\"8\">1. What is the Agent Loop?<\/h4><p data-path-to-node=\"9\">At the heart of every AI agent is a cycle called the \u201cagent loop.\u201d This loop orchestrates the interaction between the user, the model, and the tools. In Codex, the process works as follows:<\/p><p data-path-to-node=\"9\">\u00a0<\/p><ul data-path-to-node=\"10\"><li><p data-path-to-node=\"10,0,0\"><b data-path-to-node=\"10,0,0\" data-index-in-node=\"0\">Prompt Building:<\/b> User instructions, system guidelines, and tool definitions are structured into a \u201clist of items\u201d and presented to the model.<\/p><\/li><li><p data-path-to-node=\"10,1,0\"><b data-path-to-node=\"10,1,0\" data-index-in-node=\"0\">Inference:<\/b> The model processes the prompt and generates a response.<\/p><\/li><li><p data-path-to-node=\"10,2,0\"><b data-path-to-node=\"10,2,0\" data-index-in-node=\"0\">Tool Call:<\/b> The model doesn\u2019t just talk; it can request a tool call (e.g., \u201crun ls and report the output\u201d). Codex executes this tool call in the local environment and appends the result back to the original prompt.<\/p><\/li><li><p data-path-to-node=\"10,3,0\"><b data-path-to-node=\"10,3,0\" data-index-in-node=\"0\">Result:<\/b> This cycle repeats until the model stops emitting tool calls and instead produces an Assistant Message, signaling that the task is complete.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1d1f2a65 e-flex e-con-boxed tcg-animation-none e-con e-parent\" data-id=\"1d1f2a65\" data-element_type=\"container\" data-settings=\"{&quot;tc_container_hover_selector&quot;:&quot;container&quot;,&quot;tc_container_background_parallax&quot;:&quot;no&quot;,&quot;tc_smooth_scroll_effects&quot;:&quot;none&quot;,&quot;tc_css_effects&quot;:&quot;none&quot;,&quot;tc_container_clip_path&quot;:&quot;none&quot;,&quot;tcg_advanced_hover&quot;:&quot;no&quot;,&quot;float_cursor&quot;:&quot;no&quot;,&quot;tc_dark_mode_responsive_hide_in_dark&quot;:&quot;no&quot;,&quot;tc_dark_mode_responsive_hide_in_light&quot;:&quot;no&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3bd73ae7 tcg-animation-none elementor-widget elementor-widget-text-editor\" data-id=\"3bd73ae7\" data-element_type=\"widget\" data-settings=\"{&quot;tc_smooth_scroll_effects&quot;:&quot;none&quot;,&quot;tc_css_effects&quot;:&quot;none&quot;,&quot;tc_dark_mode_responsive_hide_in_dark&quot;:&quot;no&quot;,&quot;tc_dark_mode_responsive_hide_in_light&quot;:&quot;no&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h4 data-path-to-node=\"11\">2. Smart Context Management and Performance<\/h4><p data-path-to-node=\"12\">As a conversation grows, so does the length of the prompt, which can eventually exhaust the model\u2019s context window. Codex manages this with two critical strategies:<\/p><ul data-path-to-node=\"13\"><li><p data-path-to-node=\"13,0,0\"><b data-path-to-node=\"13,0,0\" data-index-in-node=\"0\">Prompt Caching:<\/b> To avoid re-processing the same information, Codex utilizes prompt caching. By placing static content (like instructions) at the beginning, it reuses computation from previous inference calls, making the process linear and highly efficient.<\/p><\/li><li><p data-path-to-node=\"13,1,0\"><b data-path-to-node=\"13,1,0\" data-index-in-node=\"0\">Compaction:<\/b> When the token limit is reached, Codex uses a compaction endpoint. It summarizes the conversation into a smaller, representative list of items, allowing the agent to continue the work without losing the \u201clatent understanding\u201d of the project.<\/p><\/li><\/ul><h4 data-path-to-node=\"14\">3. Security and Flexibility<\/h4><p data-path-to-node=\"15\">Since Codex CLI operates locally on your machine, safety is paramount.\n\nSandbox: Through a sandbox mechanism, you have total control over file permissions and network access.\n\nFlexibility: It is highly configurable, allowing you to use different Responses API endpoints\u2014whether through OpenAI, Azure, or local setups like Ollama and LM Studio.<\/p><h4 data-path-to-node=\"16\">Why It Matters ?<\/h4><p data-path-to-node=\"17\">The ability of agents to not only generate text but also interact with the file system and execute terminal commands is fundamentally transforming software development. <b data-path-to-node=\"17\" data-index-in-node=\"161\">TAO AI LAB<\/b> , we are closely monitoring these \u201cautonomous workflows,\u201d as we believe they will define the standard way of working in the future.<\/p><p data-path-to-node=\"18\">If you want to dive deeper into the technical mechanics of Codex, you can read OpenAI\u2019s original post <a class=\"ng-star-inserted\" href=\"https:\/\/openai.com\/index\/unrolling-the-codex-agent-loop\/\" target=\"_blank\" rel=\"noopener\" data-hveid=\"0\" data-ved=\"0CAAQ_4QMahcKEwj32eyt2q2SAxUAAAAAHQAAAAAQTQ\">here<\/a> or explore their open-source repository on GitHub.<\/p><hr data-path-to-node=\"19\" \/><p data-path-to-node=\"20\"><b data-path-to-node=\"20\" data-index-in-node=\"0\">What are your thoughts?<\/b> Have you started integrating autonomous agents like these into your development cycles? Let\u2019s discuss in the comments!<\/p><p data-start=\"5462\" data-end=\"5715\">Reference: 1. https:\/\/openai.com\/index\/unrolling-the-codex-agent-loop\/<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Yapay Zeka Ajanlar\u0131n\u0131n Kalbine Yolculuk: Codex &#8220;Agent Loop&#8221; Nas\u0131l \u00c7al\u0131\u015f\u0131r? Yaz\u0131l\u0131m d\u00fcnyas\u0131nda yapay zeka ajanlar\u0131 art\u0131k sadece birer yard\u0131mc\u0131 de\u011fil, i\u015f ak\u0131\u015flar\u0131m\u0131z\u0131n ayr\u0131lmaz birer par\u00e7as\u0131 haline geldi. Bug\u00fcn, OpenAI&#8217;\u0131n yerel yaz\u0131l\u0131m ajan\u0131 Codex CLI&#8216;\u0131n mutfa\u011f\u0131na giriyor ve bu sistemin &#8220;beyni&#8221; say\u0131lan Agent Loop (Ajan D\u00f6ng\u00fcs\u00fc) kavram\u0131n\u0131 inceliyoruz. Bir yapay zeka ajan\u0131, verdi\u011finiz komutu nas\u0131l anlar ve bilgisayar\u0131n\u0131zda nas\u0131l ger\u00e7ek dosyalar &hellip;<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4725","post","type-post","status-publish","format-standard","hentry","category-yapay-zeka"],"_links":{"self":[{"href":"https:\/\/taoailab.com\/en\/wp-json\/wp\/v2\/posts\/4725","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/taoailab.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/taoailab.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/taoailab.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/taoailab.com\/en\/wp-json\/wp\/v2\/comments?post=4725"}],"version-history":[{"count":5,"href":"https:\/\/taoailab.com\/en\/wp-json\/wp\/v2\/posts\/4725\/revisions"}],"predecessor-version":[{"id":4778,"href":"https:\/\/taoailab.com\/en\/wp-json\/wp\/v2\/posts\/4725\/revisions\/4778"}],"wp:attachment":[{"href":"https:\/\/taoailab.com\/en\/wp-json\/wp\/v2\/media?parent=4725"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/taoailab.com\/en\/wp-json\/wp\/v2\/categories?post=4725"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/taoailab.com\/en\/wp-json\/wp\/v2\/tags?post=4725"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}