@Jerry_fsy: I have developed a learning path for Agent Infra: I referenced some materials and then created a learning plan for Agen…

X AI KOLs Timeline News
agent-infra learning-path agents distributed-systems education open-source

Summary

一位开发者分享了自己制定的 Agent Infra 六个月学习路径，涵盖 Agent Runtime、分布式系统与可靠性、编排、沙箱、可观测性及生产级 Agent 平台六大板块，并推荐了相关课程与学习资源。

I have developed a learning path for Agent Infra: I referenced some materials and then created a learning plan for Agent Infra for myself (estimated to take about 6 months of study time; everyone can adjust according to their own pace—the focus is on mastering each part rather than pursuing speed). I summarized the previous Agent Infra learning checklist into 6 major sections, prioritizing these 6 first, and delving deeper into others as they come up. Currently, I've mainly outlined specific learning plans for the first 2 sections—those interested can join in and learn together: 1. Agent Runtime Basics Implement an Agent Loop from scratch without using existing Agent frameworks, to understand the Agent's execution process. For this, I'll follow "Sanyuan Classmate's" "Super Agent Practical Course" and build it from the ground up; in this round, I'll understand every step of the Agent's execution from the code details. Considering code copyright, in this stage, I'll only share how to approach each step from a macro perspective—those interested can build their own Agent from 0 with AI assistance. Knowledge points to understand: Agent Loop; Tool Calling; Context Management; Streaming; Agent State; MCP; Multi-Agent. 2. Distributed Systems and Reliability By learning "task queues," "communication," "state," and "reliability," implement a recoverable task execution system. It needs to ensure that after restarting a process, unfinished tasks can still continue. For getting started with this section, the book DDIA is quite challenging. For now, I'll first consider using the open-source learning resource System Design 101, supplemented by MIT 6.1800 to fill in computer systems-related knowledge, and MIT 6.5840 to supplement distributed systems knowledge (these two MIT courses will be studied selectively). Knowledge points to understand: Process/Thread/Async; RPC/HTTP/gRPC; Queue/PubSub; State Machine; Timeout/Retry; Idempotency; Transaction/WAL, etc. 3. Agent Orchestration 4. Sandbox and Isolated Execution 5. Observability and Evaluation 6. Production Agent Platform
Original Article
View Cached Full Text

Cached at: 10/12/26, 05:30 PM

I have developed a learning path for Agent Infra:

I referenced some materials and then created a learning plan for Agent Infra for myself (estimated to take about 6 months of study time; everyone can adjust according to their own pace—the focus is on mastering each part rather than pursuing speed).

I summarized the previous Agent Infra learning checklist into 6 major sections, prioritizing these 6 first, and delving deeper into others as they come up. Currently, I’ve mainly outlined specific learning plans for the first 2 sections—those interested can join in and learn together:

  1. Agent Runtime Basics Implement an Agent Loop from scratch without using existing Agent frameworks, to understand the Agent’s execution process. For this, I’ll follow “Sanyuan Classmate’s” “Super Agent Practical Course” and build it from the ground up; in this round, I’ll understand every step of the Agent’s execution from the code details. Considering code copyright, in this stage, I’ll only share how to approach each step from a macro perspective—those interested can build their own Agent from 0 with AI assistance. Knowledge points to understand: Agent Loop; Tool Calling; Context Management; Streaming; Agent State; MCP; Multi-Agent.

  2. Distributed Systems and Reliability By learning “task queues,” “communication,” “state,” and “reliability,” implement a recoverable task execution system. It needs to ensure that after restarting a process, unfinished tasks can still continue. For getting started with this section, the book DDIA is quite challenging. For now, I’ll first consider using the open-source learning resource System Design 101, supplemented by MIT 6.1800 to fill in computer systems-related knowledge, and MIT 6.5840 to supplement distributed systems knowledge (these two MIT courses will be studied selectively). Knowledge points to understand: Process/Thread/Async; RPC/HTTP/gRPC; Queue/PubSub; State Machine; Timeout/Retry; Idempotency; Transaction/WAL, etc.

  3. Agent Orchestration

  4. Sandbox and Isolated Execution

  5. Observability and Evaluation

  6. Production Agent Platform

Similar Articles

@Jerry_fsy: If you're interested in AI-related work, you might consider the emerging field of Agent Infra. I've recently been getti…

X AI KOLs Timeline

推文整理了 Agent Infra（智能体基础设施）这一新兴领域的 12 项核心技能，涵盖 Agent 运行时、上下文与状态管理、任务队列、多智能体编排、持久化执行、沙箱环境、MCP 工具协议、权限安全、可观测性与评估等方面，为有意进入该方向的开发者提供学习路线图。

@vintcessun: Tonight I came across a learning roadmap project that redefined where to start learning Agent. I used to think Agent was just a pile of tools and frameworks, but its core is the "observe-think-execute" loop and the harness engineering's organization of permissions, state, and backtracking. It breaks down learning into building a minimal Agent loop from scratch all the way to deploying a real Agent, with 8 stages, each with clear deliverables and recommended resources — not just links but an actionable todo list. This systematic approach made me realize my previous learning was too fragmented.

X AI KOLs Timeline

An open-source learning roadmap project called Agent-Learning-Hub, which breaks down AI Agent learning into 8 stages from building a minimal Agent loop to production deployment, providing executable todo lists and recommended resources, maintained by members of the Datawhale community.

@Xudong07452910: Free and Open-Source High-Quality Tutorial Recommendation: 'Building an Agent from Scratch' - A systematic tutorial on Agent principles and practice from zero to advanced, covering: 1. Basic concepts and mainstream paradigms (ReAct, Plan-and-Solve, Reflection, etc.) 2.…

X AI KOLs Timeline

Recommend the free and open-source tutorial 'Building an Agent from Scratch', which systematically explains AI Agent principles and practice, covering mainstream frameworks such as ReAct, AutoGen, LangGraph, and multiple hands-on projects. It has received 53,000+ stars.

@XAMTO_AI: Guys, still learning AI Agent in bits and pieces? Check this out! A 22-chapter skeleton course on building a production-grade Agent system from 0 to 1. Tool calling, Agent loop, memory system, multi-Agent collaboration, human-machine interaction, backend architecture, observability, cost optimization, security... The coolest part is the core gameplay — using Agent to teach Agent. The author only provides the skeleton, and the AI assistant fills in the code and details. Learn by doing. This is the learning style for 2026; the era of rote memorization is over.

X AI KOLs Timeline

A 22-chapter skeleton course on building production AI agents, using an innovative approach where the AI partner fills in details. The course covers tool calling, agent loops, memory, multi-agent collaboration, and more.