2026 接近 Devin 架構，包含：
- Hierarchical Planning Agent
- Agent Society (10+ agents)
- Autonomous Software Company Simulation
- Self-Improving AI
- Continuous Learning Loop
設計目標是打造一個 Autonomous Software Company：一個 AI 系統可以像一間軟體公司一樣運作，自己規劃、研究、寫程式、測試、部署並持續改進。
能力對齊：
- Devin
- OpenDevin
- AutoGPT
- LangGraph
一、整體概念：Autonomous AI Software Company
系統模擬一間公司：CEO Agent
│
▼
CTO Agent
│
▼
Engineering Teams
│
▼
Developers / QA / DevOps Agents
整體流程：
User goal
↓
CEO agent (define product)
↓
CTO agent (architecture)
↓
Planning hierarchy
↓
Agent society execution
↓
Code + repo generation
↓
Testing + debugging
↓
Deployment
↓
Learning loop
二、整體系統架構
USER GOAL
│
▼
Strategic CEO Agent
│
▼
CTO Architecture
│
┌──────────────┼───────────────┐
▼ ▼
Hierarchical Planner Knowledge System
│ │
▼ ▼
Task Tree Engine Vector + Graph Memory
│
▼
Agent Society (10+)
│
▼
Execution Infrastructure
│
┌───────┼────────┐
▼ ▼
Browser Agent Terminal Agent
│ │
▼ ▼
Research Code Runner
│
▼
Repo Builder
│
▼
Testing Pipeline
│
▼
Reflection + Critic
│
▼
Continuous Learning
三、Hierarchical Planning Agent
Hierarchical planning = 多層規劃系統。
層級：
Goal
├─ Product Plan
│ ├─ Feature
│ │ ├─ Task
│ │ │ └─ Code step
Python結構：
</>Python
class PlanNode:
def __init__(self,name,level):
self.name = name
self.level = level
self.children = []
self.status = "pending"
def add_child(self,node):
self.children.append(node)
Planner agent：
</>Python
class HierarchicalPlanner:
def create_plan(self,goal):
root = PlanNode(goal,"goal")
feature = PlanNode("design feature","feature")
task = PlanNode("implement module","task")
root.add_child(feature)
feature.add_child(task)
return root
四、Agent Society（10+ Agents）
AI 不再是單一 agent，而是 多 agent 社會。
Agent清單
CEO Agent
CTO Agent
Planner Agent
Research Agent
Architect Agent
Coding Agent
Testing Agent
Debug Agent
DevOps Agent
Critic Agent
Memory Agent
Tool Engineer Agent
五、Agent Society Orchestrator
</>Python
class AgentSociety:
def __init__(self):
self.agents = {}
def register(self,name,agent):
self.agents[name] = agent
def assign(self,task):
if "research" in task:
return self.agents["research"]
if "code" in task:
return self.agents["coding"]
return self.agents["planner"]
六、Autonomous Repo Builder
AI 自動生成完整 repo。
結構：
project/
├─ src/
│ ├─ api.py
│ ├─ models.py
├─ tests/
├─ requirements.txt
└─ README.md
Repo builder：
</>Python
import os
class RepoBuilder:
def create_repo(self,name):
os.makedirs(name+"/src",exist_ok=True)
os.makedirs(name+"/tests",exist_ok=True)
def write_file(self,path,content):
with open(path,"w") as f:
f.write(content)
七、Code Interpreter
類似 ChatGPT Code Interpreter。
</>Python
import subprocess
import tempfile
class CodeInterpreter:
def execute(self,code):
with tempfile.NamedTemporaryFile(
suffix=".py",delete=False
) as f:
f.write(code.encode())
path = f.name
result = subprocess.run(
["python",path],
capture_output=True,
text=True
)
return result.stdout
八、Browser + Terminal Automation
Browser：
</>Python
from playwright.sync_api import sync_playwright
def browse(url):
with sync_playwright() as p:
browser = p.chromium.launch()
page = browser.new_page()
page.goto(url)
content = page.inner_text("body")
browser.close()
return content
Terminal：
</>Python
import subprocess
def run_cmd(cmd):
result = subprocess.run(
cmd,
shell=True,
capture_output=True,
text=True
)
return result.stdout
九、Self-Improving AI
AI 可以 改進自己。
Self-improvement loop：
task result
↓
critic agent
↓
identify failure
↓
modify tool
↓
test tool
↓
deploy new tool
Tool learning：
</>Python
class ToolLearner:
def __init__(self):
self.tools = {}
def learn_tool(self,name,code):
self.tools[name] = code
十、Continuous Learning Loop
整個系統會 持續學習。
Task
↓
Execution
↓
Result
↓
Reflection
↓
Memory update
↓
Model fine-tuning
↓
Better agent
Pseudo code：
</>Python
while True:
task = planner.next_task()
result = executor.run(task)
review = critic.evaluate(result)
memory.store(task,result)
if review == "success":
break
十一、Memory 系統
兩層：
Vector memory（RAG）
FAISS
Milvus
Chroma
Embedding：
bge-m3
Graph memory
追蹤：
tasks
files
functions
dependencies
Graph DB：
Neo4j
NetworkX
十二、Workspace Sandbox
AI 必須在 sandbox 執行程式。
推薦：
Docker
Kubernetes
Firecracker VM
Docker manager：
</>Python
import docker
class Sandbox:
def __init__(self):
self.client = docker.from_env()
def run(self,image):
return self.client.containers.run(
image,
detach=True
)
十三、4090 本地配置（推薦）
LLM：
Qwen2.5 14B
DeepSeek-Coder 33B (4bit)
Embedding：
bge-m3
Agent orchestration：
LangGraph
十四、完整 Autonomous Company Loop
最終流程：
User goal
↓
CEO agent
↓
CTO architecture
↓
Hierarchical planning
↓
Agent society execution
↓
Research (browser)
↓
Code generation
↓
Repo creation
↓
Testing
↓
Debugging
↓
Deployment
↓
Reflection
↓
Learning
↓
Next task
十五、完成後能力接近
- Devin
- OpenDevin
- AutoGPT
可以做到：
自動建立 SaaS
自動寫 backend
自動 debug
自動寫測試
自動 deploy
