A hands-on roadmap for building production-ready AI Agents, MCP Servers, Memory Systems, Multi-Agent Workflows, and Agent Colonies.

繁體中文 · Website · Course · Roadmap · Examples · Showcases · Benchmarks · Labs · Teaching · Templates · Architecture · Healthcare · Finance

flowchart LR
    User((User)) --> Agent[AI Agent]
    Agent --> Tools[Tool Use]
    Tools --> MCP[MCP Layer]
    MCP --> Memory[Memory System]
    Memory --> Workflow[Agent Workflow]
    Workflow --> MultiAgent[Multi-Agent Team]
    MultiAgent --> Colony[Agent Colony]
    Colony --> Production[Production AI App]

Why this roadmap exists

Most AI tutorials stop at prompts, RAG, or simple tool calling.

Real agentic products require more than that:

• agents that can use tools safely
• MCP servers that connect agents to real systems
• memory layers that persist useful context
• workflows that are observable and controllable
• multi-agent teams that can specialize and collaborate
• evaluation, security, and production guardrails

This repository is a practical learning path for builders who want to move from chatbot demos to real agent engineering.

Teaching approach

This roadmap teaches agents like an engineering course, not a tool catalog.

Each major topic follows the same pattern:

1. Start with the problem: what breaks if you only use a chatbot?
2. Build the intuition: what is the simplest mental model?
3. Open the box: what components are actually involved?
4. Run a minimal example: what can you inspect locally?
5. Add production judgment: what needs evaluation, observability, approval, or safety gates?

In one sentence: an agent is not magic. It is context, tools, memory, workflow, evaluation, and human judgment arranged around a useful task.

What you will learn

Course materials

The learning path

AI Fundamentals
      ↓
Single Agent
      ↓
Tool Use
      ↓
MCP Integration
      ↓
Agent Memory
      ↓
Agent Workflow
      ↓
Multi-Agent Systems
      ↓
Agent Colony
      ↓
Production, Evaluation & Safety

Try it in 60 seconds

Run a showcase without API keys:

python showcases/enterprise-support-agent/main.py
python showcases/finance-research-agent/main.py
python showcases/healthcare-agent-colony/main.py

Then run the evaluation harness:

python examples/07-evaluation-harness/main.py
python examples/08-mini-rag/main.py
python benchmarks/benchmark_runner.py
python scripts/verify_examples.py

Production readiness artifacts

Showcase demos

Runnable examples

Run every dependency-free example with:

python scripts/verify_examples.py

README widgets used

This README uses lightweight visual widgets commonly seen in popular GitHub projects:

• Local cover image for the top hero banner
• shields.io for stars, forks, language, status, and topic badges
• Mermaid for architecture diagrams

Plugin ecosystem

Agent Engineering is not only about prompts. A production agent needs a plugin ecosystem around it.

Core architecture

graph TD
    User[User] --> Supervisor[Supervisor Agent]
    Supervisor --> Planner[Planner]
    Planner --> MemoryAgent[Memory Agent]
    Planner --> ResearchAgent[Research Agent]
    Planner --> ToolAgent[Tool Agent]
    Planner --> DomainAgent[Domain Agent]
    MemoryAgent --> SharedMemory[Shared Memory]
    ToolAgent --> MCP[MCP Servers]
    DomainAgent --> MCP
    ResearchAgent --> MCP
    MCP --> PluginLayer[Plugin Ecosystem]
    PluginLayer --> Databases[Databases]
    PluginLayer --> Documents[Documents]
    PluginLayer --> APIs[External APIs]
    PluginLayer --> SaaS[SaaS Apps]
    Supervisor --> Evaluator[Evaluator Agent]
    Evaluator --> Final[Final Response]
    Final --> User
    Evaluator --> SharedMemory

Repository structure

agent-engineering-roadmap/
├── README.md
├── README_zh.md
├── COURSE.md
├── assets/           # Visual diagrams and teaching images
├── roadmap/          # Level 0-8 learning path
├── curriculum/       # Full course chapters
├── examples/         # Hands-on examples
├── benchmarks/       # Lightweight behavior checks
├── security/         # Prompt injection and agent security labs
├── study-groups/     # Cohort and workshop facilitation kit
├── showcases/        # Shareable demos with sample outputs
├── labs/             # Guided exercises
├── lesson-plans/     # Instructor-ready lesson plans
├── patterns/         # Architecture pattern catalog
├── architecture/     # System design patterns
├── templates/        # Reusable agent and MCP templates
├── assessments/      # Quiz bank and rubrics
├── projects/         # Capstone and portfolio projects
├── glossary/         # Agent engineering terms
├── healthcare/       # Healthcare agent engineering track
├── finance/          # Finance and quantitative research track
├── resources/        # Curated learning resources
├── docs/             # GitHub Pages site
└── launch-kit/       # Launch copy, topics, and checklist

Real-world tracks

Healthcare Agent Engineering

Build agent systems for care management, nutrition tracking, personal health memory, and healthcare workflow automation.

Example colony:

Care Manager Agent
├── Nutrition Agent
├── Vital Sign Agent
├── Psychology Agent
├── Medication Agent
├── Memory Agent
└── Safety Evaluator Agent

Finance Agent Engineering

Build research agents, factor-analysis agents, portfolio agents, risk agents, and trading research workflows.

Example colony:

Research Agent
├── Market Data Agent
├── Factor Analysis Agent
├── Portfolio Agent
├── Risk Agent
└── Report Agent

Enterprise Agent Engineering

Build customer support agents, internal knowledge agents, document agents, workflow automation agents, and evaluation pipelines.

Design principles

1. Agents should be useful before they are autonomous.
2. Memory should be intentional, auditable, and safe.
3. MCP should be treated as an integration layer, not just a plugin mechanism.
4. Multi-agent systems should reduce complexity for users, not create complexity for developers.
5. Production agents need evaluation, observability, cost control, and human approval gates.

Project roadmap

• Initialize bilingual repository structure
• Add Level 0-8 roadmap skeleton
• Add architecture documents
• Add healthcare and finance tracks
• Add README badges and hero banner
• Expand each roadmap level into handbook chapters
• Add minimal runnable examples
• Add MCP server templates
• Add memory system examples
• Add agent colony demo
• Add evaluation and safety templates
• Add full course syllabus
• Add observable agent and prompt injection defense examples
• Add benchmark runner and study group kit
• Add cost, durable runtime, and modern MCP gateway modules
• Add memory governance, identity permission, and incident response modules
• Add advanced eval, product UX, and enterprise operating model modules
• Add guided labs
• Add instructor-ready lesson plans
• Add pattern catalog
• Add quiz bank, rubrics, glossary, and capstone
• Add full healthcare agent colony application
• Add full finance research agent application

Who this is for

• AI engineers
• LLM application developers
• Startup builders
• Researchers building agent systems
• Product teams moving from chatbot demos to real workflows
• Developers interested in MCP, memory, and multi-agent systems

License

This project is licensed under the MIT License.