AI Agent Engineer

AI Agent Engineer

OPEN ENGINEERING ROADMAP · 2026

Build, evaluate, and deploy AI Agent

An engineering learning base for real delivery. Choose from 57 learning focuses by role and capability gap, spanning Python, APIs, and data foundations through RAG, agents, evaluation, governance, and complex collaboration across 10 knowledge domains.

View the full roadmap

ROLE TRACKS

Choose a delivery goal, then build the capabilities

The four tracks read course v2 metadata directly. Core, recommended, and optional courses keep distinct boundaries instead of presenting suggestions as hard dependencies.

01Agent application development

Deliver a stateful, approvable, recoverable single agent.

9 core · 13 recommended · 10 optional02RAG and knowledge bases

Deliver a dual-pipeline RAG system with ACLs, citations, and layered evaluation.

17 core · 12 recommended · 6 optional03Agent platform and reliability

Deliver a runtime platform with release gates, traces, rollback, and governance.

14 core · 16 recommended · 8 optional04Multimodal and real-time interaction

Deliver an interruptible real-time multimodal prototype that can use tools.

10 core · 13 recommended · 6 optional

LEARNING SYSTEM

10 knowledge domains

Knowledge domains provide stable classification, role tracks provide learning order, and hard prerequisites remain only where a complete course truly cannot be skipped.

01Engineering and mathematical foundationsBuild a reliable foundation in programming, data representation, collaboration, and quantitative reasoning.14 learning focuses02Models and contextChoose models and control prompts, context budgets, and API boundaries.4 learning focuses03Retrieval and dataBuild cleaning, parsing, chunking, retrieval, reranking, and citation paths.11 learning focuses04Multimodal systemsWork with document vision, speech, real-time interaction, and generated media.7 learning focuses05Agent runtimeConstrain tools, state, permissions, recovery, collaboration, and stop conditions.8 learning focuses06Framework practiceChoose replaceable assembly frameworks after understanding the underlying contracts.2 learning focuses07Evaluation and reliabilityUse datasets, graders, visualization, and stress cases to establish quality evidence.4 learning focuses08Safety and governanceBring threats, privacy, responsibility, and compliance into the lifecycle.3 learning focuses09Production operationsManage releases, rollbacks, monitoring, and model or application artifacts.3 learning focuses10Frontier and referenceStudy rapidly changing protocols and research directions with explicit observation status.1 learning focuses

57 LEARNING FOCUSES

All learning focuses

Every entry leads to a unified course overview; the reading-page sidebar preserves the real course and subfolder hierarchy.

01

Engineering and mathematical foundations

01AI Foundations Learning Path02Python Fundamentals03Data Structures Fundamentals04JSON Learning Index05API Learning Path06Markdown07Git Learning Path08Linux Commands09Regular Expressions10Probability and Statistics11Linear Algebra12Calculus Fundamentals13Machine Learning14Deep Learning
02

Models and context

18.5Modern LLM Capabilities and Model Selection19Prompt Engineering20Context Engineering Learning Path21LLM API Integration Learning Path
03

Retrieval and data

15Vector Fundamentals16Data Cleaning17Data Annotation Learning Path22Document Parsing23Knowledge Base Construction24Chunking Strategies25Embeddings26Vector Databases27Semantic Search Learning Path28Reranking29RAG Learning Path
04

Multimodal systems

48Multimodal AI Learning Path49OCR50Speech Recognition51Text to Speech51.5Real-Time Multimodal Interaction52Image Generation Learning Path53Video Generation Learning Path
05

Agent runtime

30Tool Calling (including Function Calling)31MCP32Agent Core32.5Environment-based Agents33Agent Skills Learning Path34Agentic Design Patterns35Workflow Automation Learning Path47Multi-Agent Collaboration Learning Path
06

Framework practice

36LangChain37CrewAI Learning Index
07

Evaluation and reliability

18Data Visualization39Evaluation Framework Learning Path42Benchmark Design Learning Path43Synthetic Data
08

Safety and governance

44AI Safety Learning Path45Privacy-Enhancing Technologies Learning Path46AI Governance Learning Path
09

Production operations

38MLOps Learning Path40Runtime Monitoring Learning Path41LLMOps Learning Path
10

Frontier and reference

54A2A Protocol

LANDMARK READINGS

Trace agent evolution through 12 defining works

A source-grounded reading library separate from courses and examples, using original diagrams, evidence boundaries, and study prompts to make papers and engineering posts approachable.

RD · SOURCEAI agent landmark readingsFollow the line from ReAct, Generative Agents, and MCP to SWE-agent, context engineering, and harness engineering.12 readings · bilingual

EXAMPLE PROJECTS

Learn agent engineering from real source

The example library stays separate from formal courses and progress tracking while connecting source reading, runtime observation, troubleshooting, and extension practice.

EX · SOURCEnanobot source-learning libraryTrace one message through AgentLoop, providers, tools, sessions, and long-running boundaries, then complete progressive source exercises.8 stages · bilingual

AI Agent Engineer

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