Everything you need to know about the typical ai agent stack explained.
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This week’s system design refresher:
How to Run LLMs Locally (Youtube video)
The Typical AI Agent Stack, Explained
Understanding Git Reset Modes
How NAT Works
Final Week to Enroll: Build with Claude Code
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Most 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.
The 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.
AI 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.
Model Layer (the brain): The underlying LLMs that power reasoning.
Tool Layer (the hands): How the agent interacts with the real world: search, APIs, code execution, data access.
Memory Layer (the notebook): Short-term working memory for the current task, long-term semantic memory for knowledge, and transactional memory for state.
Wrapping everything is the Observability & Safety Layer. This is what keeps agents debuggable, evaluable, cost-aware, and safe in production.
Over to you: Which layer of the stack do you think is the hardest to get right in production?
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git reset has three modes. Each one moves HEAD, but they differ in what happens to your index and working directory.
git 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.
git 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.
git reset –hard: Moves HEAD, clears the index, and resets the working directory to match the target commit. Any uncommitted changes are gone.
Over to you: Which reset mode do you use the most and has “–hard” ever cost you a day of work?
Every 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.
It’s the reason IPv4 hasn’t run out completely, and why your router can hide dozens of devices behind a single public IP.
The 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.
NAT rewrites each outbound request so it appears to come from that public IP address, assigning a unique port mapping for every internal connection.
Outbound NAT (Local to Internet): When a device sends a request,
NAT replaces the private IP address with the public one
Assigns a unique port so it can track the connection
Sends the packet out to the internet as if it originated from the router
Reverse NAT (Internet to Local): When the response returns,
NAT checks its translation table
Restores the original private IP address and port
Delivers the packet to the correct device on the local network
Over to you: Have you ever run into tricky NAT edge cases? Port forwarding? Double NAT? Video calls breaking? Online gaming problems?
We’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.
The 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.
A few things you’ll learn:
The agentic loop, context engineering, and memory layers that make Claude Code useful for real projects
How to build with Claude Code Skills, MCPs, and hooks to give Claude the tools and feedback loops it needs to self correct
Parallel development with Git worktrees, subagents, and agent teams
A capstone project where you ship something real on your own stack
The course includes live sessions, assignments, and office hours, so there’s plenty of room to ask questions and get unstuck.
The 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.
We’re looking for multiple part-time instructors to teach AI and engineering cohort-based live courses.
This is a great fit if you love teaching, enjoy sharing what you know, and want a meaningful side thing alongside your main work.
The 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.
We’re especially looking for instructors in:
Building Production-Grade AI Systems
System Design
AI Security & LLM Red-Teaming
AI Evals Intensive
AI Cost Optimization
Agentic AI Coding
Build with Codex
AI for Engineering Leaders
AI Automation
Others, please suggest
Ideal instructors are hands-on, clear communicators, and excited to teach.
If 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.
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More on the typical ai agent stack explained
Stay tuned for updates on the typical ai agent stack explained as the story develops. Related: EP219: 12 Open-source LLMs – ByteByteGo Newsletter.
Frequently Asked Questions
The Agent Runtime running a ReAct loop at the core, fed by the Model Layer (the brain), the Tool Layer (the hands) and the Memory Layer (the notebook), all wrapped in an Observability and Safety Layer.
The LLM thinks about what to do, picks a tool, observes the result, then reflects and decides the next step, repeating the loop until the goal is reached.
Short-term working memory for the current task, long-term semantic memory for knowledge, and transactional memory for state.
A remote MCP server that connects AI agents to the Descope identity platform, letting them read docs, manage users and tenants, configure authentication flows and review audit logs through natural language in a single session.
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