{"slug": "ep218-the-typical-ai-agent-stack-explained", "title": "EP218: The Typical AI Agent Stack, Explained", "summary": "ByteByteGo's newsletter explains the typical AI agent stack, which consists of an agent runtime running a ReAct loop, a model layer, a tool layer, and a memory layer, all wrapped in an observability and safety layer. The article also covers how to run LLMs locally, Git reset modes, and how NAT works.", "body_md": "# EP218: The Typical AI Agent Stack, Explained\n\n[Run your customer auth with AI agents (Sponsored)](https://go.bytebytego.com/Descope_061326)\n\nCoding agents are here to stay, but vibe-coding auth is dangerous business. Connect your AI assistants to the Descope MCP server instead!\n\nThis remote MCP server connects agents to the Descope identity platform, giving them the ability to read docs, inspect project config, manage users and tenants, configure authentication flows, review audit logs, and make changes to your identity infrastructure. All through natural language and from a single session.\n\nDescope is trusted by thousands of businesses including GoFundMe, GoodRx, Linktree, and Databricks.\n\nThis week’s system design refresher:\n\nHow to Run LLMs Locally (Youtube video)\n\nThe Typical AI Agent Stack, Explained\n\nUnderstanding Git Reset Modes\n\nHow NAT Works\n\nFinal Week to Enroll: Build with Claude Code\n\nWe’re hiring at ByteByteGo\n\n## How to Run LLMs Locally (Great For Learning and Privacy)\n\n## The Typical AI Agent Stack, Explained\n\nMost people think an AI agent is just a clever prompt and an LLM. The reality is much deeper. There's an entire architecture working behind the scenes to make it all run.\n\nThe diagram below shows the full AI Agent Stack. At the core is the Agent Runtime that runs a ReAct loop, and three other layers feed into it.\n\nAI Agent Runtime: The LLM thinks about what to do, picks a tool, observes the result, then reflects and decides the next step. This loop repeats until the goal is reached.\n\nModel Layer (the brain): The underlying LLMs that power reasoning.\n\nTool Layer (the hands): How the agent interacts with the real world: search, APIs, code execution, data access.\n\nMemory Layer (the notebook): Short-term working memory for the current task, long-term semantic memory for knowledge, and transactional memory for state.\n\nWrapping everything is the Observability & Safety Layer. This is what keeps agents debuggable, evaluable, cost-aware, and safe in production.\n\nOver to you: Which layer of the stack do you think is the hardest to get right in production?\n\n[FeatureOps Summit 2026 - Feature management in the AI Era (Sponsored)](https://go.bytebytego.com/Unleashed_061326)\n\nSpeed without control is a false economy. As AI code-generation accelerates software delivery, the FeatureOps Summit 2026 is here to ensure that when we ship more, we break less. This premier virtual event brings together engineers, architects, and product leaders from companies like Wayfair, Visa, Mintlify, Lloyds, and many others, to explore the infrastructure of fearless delivery.\n\n**Key Themes:**\n\n**AI Safety Nets:** Guardrails for the flood of automated code.**Edge Resilience:** Sub-millisecond evaluation at scale.**Continuous Flow:** Moving past the “fixed-release” mindset. Register today to master the tools and patterns required for a fail-safe release environment.\n\n## Understanding Git Reset Modes\n\ngit reset has three modes. Each one moves HEAD, but they differ in what happens to your index and working directory.\n\ngit reset --soft: Moves HEAD only. Index and working directory stay as-is. Use this when you want to recommit with different changes or a different message.\n\ngit reset --mixed (default): Moves HEAD and clears the index, but leaves the working directory alone. Your changes become unstaged, still there, just no longer queued for commit.\n\ngit reset --hard: Moves HEAD, clears the index, and resets the working directory to match the target commit. Any uncommitted changes are gone.\n\nOver to you: Which reset mode do you use the most and has “--hard” ever cost you a day of work?\n\n## How NAT Works\n\nEvery device in your home probably shares the same public IP, still each one browses, streams, and connects independently. This is handled by NAT (Network Address Translation), a protocol that runs quietly in the background of almost every home network.\n\nIt’s the reason IPv4 hasn’t run out completely, and why your router can hide dozens of devices behind a single public IP.\n\nThe Core Idea: Inside your local network, devices use private IP addresses that never leave your home or office. Your router, however, uses a single public IP address when talking to the outside world.\n\nNAT rewrites each outbound request so it appears to come from that public IP address, assigning a unique port mapping for every internal connection.\n\nOutbound NAT (Local to Internet): When a device sends a request,\n\nNAT replaces the private IP address with the public one\n\nAssigns a unique port so it can track the connection\n\nSends the packet out to the internet as if it originated from the router\n\nReverse NAT (Internet to Local): When the response returns,\n\nNAT checks its translation table\n\nRestores the original private IP address and port\n\nDelivers the packet to the correct device on the local network\n\nOver to you: Have you ever run into tricky NAT edge cases? Port forwarding? Double NAT? Video calls breaking? Online gaming problems?\n\n## Final Week to Enroll: Build with Claude Code\n\nWe’re launching a new 2 day intensive, cohort based course called Build with Claude Code, taught by John Kim, who has trained hundreds of engineers at Meta to use Claude Code in real production workflows.\n\nThe course kicks off **June 18th**, and **enrollment closes in less than a week**. If you’ve been thinking about leveling up how you and your team work with Claude Code, this is the moment.\n\nA few things you’ll learn:\n\nThe agentic loop, context engineering, and memory layers that make Claude Code useful for real projects\n\nHow to build with Claude Code Skills, MCPs, and hooks to give Claude the tools and feedback loops it needs to self correct\n\nParallel development with Git worktrees, subagents, and agent teams\n\nA capstone project where you ship something real on your own stack\n\nThe course includes live sessions, assignments, and office hours, so there’s plenty of room to ask questions and get unstuck.\n\nThe first cohort starts in just a few days: May 28 to 29, 2026. If you want to learn everything from the fundamentals of Claude Code to advanced production workflows, including working with large codebases, this could be a great way to level up.\n\n## We’re Hiring at ByteByteGo\n\nWe’re looking for multiple part-time instructors to teach AI and engineering cohort-based live courses.\n\nThis is a great fit if you love teaching, enjoy sharing what you know, and want a meaningful side thing alongside your main work.\n\nThe role has some upfront time investment to get familiar with the curriculum and prepare, but after that, it’s designed to be a limited commitment (2-5 hours bi-weekly). It offers stable income, good upside, and a chance to share your knowledge while working with ambitious learners.\n\nWe’re especially looking for instructors in:\n\nBuilding Production-Grade AI Systems\n\nSystem Design\n\nAI Security & LLM Red-Teaming\n\nAI Evals Intensive\n\nAI Cost Optimization\n\nAgentic AI Coding\n\nBuild with Codex\n\nAI for Engineering Leaders\n\nAI Automation\n\nOthers, please suggest\n\nIdeal instructors are hands-on, clear communicators, and excited to teach.\n\nIf this sounds like you, email us at ** jobs@bytebytego.com** with your background, the topics you’d be excited to teach, and any teaching, writing, or speaking samples.", "url": "https://wpnews.pro/news/ep218-the-typical-ai-agent-stack-explained", "canonical_source": "https://blog.bytebytego.com/p/ep218-the-typical-ai-agent-stack", "published_at": "2026-06-13 15:31:02+00:00", "updated_at": "2026-07-08 00:01:06.593707+00:00", "lang": "en", "topics": ["ai-agents", "large-language-models", "developer-tools"], "entities": ["ByteByteGo", "Descope", "Claude Code", "Git", "NAT"], "alternates": {"html": "https://wpnews.pro/news/ep218-the-typical-ai-agent-stack-explained", "markdown": "https://wpnews.pro/news/ep218-the-typical-ai-agent-stack-explained.md", "text": "https://wpnews.pro/news/ep218-the-typical-ai-agent-stack-explained.txt", "jsonld": "https://wpnews.pro/news/ep218-the-typical-ai-agent-stack-explained.jsonld"}}