2nd/10_Wiki/Topic_Programming/AI_and_ML/Process-Automation-with-AI.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-process-automation-with-ai	Process Automation with AI	10_Wiki/Topics	verified	self	AI RPA Intelligent Automation AI Agent Automation	none	A	0.9	applied	automation ai-agents rpa workflow llm		2026-05-10	pending	language framework Python/JavaScript n8n/Zapier/LangGraph

Process Automation with AI

매 한 줄

"매 deterministic workflow + LLM reasoning step". 2010s RPA (UiPath, Automation Anywhere) 가 매 brittle screen-scraping 이었다면, 매 2026 현재 hybrid AI agent — 매 structured workflow node + LLM decision node — 가 default. n8n + Claude tool-use 의 dominant pattern.

매 핵심

매 spectrum

• Pure RPA: 매 deterministic, 매 scripted UI clicks. UiPath, Power Automate.
• iPaaS: API-first integration. Zapier, Make, n8n.
• AI-augmented iPaaS: 매 LLM step 의 추가. Zapier AI Actions, Make AI modules.
• Agent automation: 매 LLM 의 plans + executes. Claude tool-use, LangGraph, CrewAI.
• Computer-use agent: 매 LLM 의 screen + mouse 의 직접 control. Anthropic Computer Use, OpenAI Operator.

매 architecture

• Trigger (webhook, schedule, email, file).
• Extract (LLM parses unstructured → JSON).
• Decide (LLM chooses branch / tool).
• Act (API call, DB write, send email).
• Verify (LLM judges output, human-in-loop on low confidence).

매 응용

1. Invoice processing (PDF → ERP).
2. Customer support triage (email → ticket category + draft reply).
3. Lead enrichment (CRM + LinkedIn + LLM summary).
4. Code review automation (PR → AI comments).
5. Compliance monitoring (logs → policy check).

💻 패턴

n8n + Claude (2026 standard)

{
  "nodes": [
    {"type": "webhook", "name": "Email received"},
    {"type": "anthropic", "name": "Classify",
     "params": {
       "model": "claude-opus-4-7",
       "system": "Classify email: SUPPORT, SALES, SPAM. Return JSON.",
       "input": "={{ $json.body }}"
     }},
    {"type": "switch", "rules": [
      {"value": "SUPPORT", "output": 0},
      {"value": "SALES", "output": 1}
    ]},
    {"type": "zendesk", "name": "Create ticket"}
  ]
}

LangGraph state machine

from langgraph.graph import StateGraph, END
from anthropic import Anthropic

client = Anthropic()

def classify(state):
    r = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=200,
        messages=[{"role": "user",
                   "content": f"Category? {state['text']}"}]
    )
    return {"category": r.content[0].text.strip()}

def route(state):
    return state["category"].lower()

g = StateGraph(dict)
g.add_node("classify", classify)
g.add_node("support", lambda s: {"reply": "Support team handling"})
g.add_node("sales", lambda s: {"reply": "Sales team handling"})
g.set_entry_point("classify")
g.add_conditional_edges("classify", route,
    {"support": "support", "sales": "sales"})
g.add_edge("support", END)
g.add_edge("sales", END)
app = g.compile()

Tool-use agent (Claude)

tools = [
  {"name": "create_ticket",
   "description": "Create Zendesk ticket",
   "input_schema": {"type": "object",
     "properties": {
       "subject": {"type": "string"},
       "priority": {"type": "string", "enum": ["low","high","urgent"]}
     }, "required": ["subject"]}},
  {"name": "send_slack",
   "description": "Notify channel",
   "input_schema": {"type": "object",
     "properties": {"channel": {"type":"string"}, "msg":{"type":"string"}}}}
]

resp = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=2000,
    tools=tools,
    messages=[{"role":"user", "content": email_text}]
)
for block in resp.content:
    if block.type == "tool_use":
        result = dispatch(block.name, block.input)

Computer Use (Anthropic, 2025+)

# 매 LLM 의 screenshot 의 보고 click/type.
# Brittle UI 의 RPA 의 대체.
response = client.beta.messages.create(
    model="claude-opus-4-7",
    tools=[{"type": "computer_20250124",
            "name": "computer", "display_width_px": 1920,
            "display_height_px": 1080}],
    messages=[{"role":"user",
        "content":"Open SAP, navigate to PO #4521, approve."}]
)

Verification + HITL (human-in-loop)

def confidence_gate(decision, threshold=0.85):
    if decision["confidence"] < threshold:
        send_to_human_queue(decision)
        return None
    return execute(decision["action"])

Idempotent retry

@retry(stop=stop_after_attempt(3), wait=wait_exponential())
def safe_api_call(idempotency_key, payload):
    return requests.post(url, json=payload,
                         headers={"Idempotency-Key": idempotency_key})

매 결정 기준

상황	Approach
Simple SaaS-to-SaaS sync	Zapier / Make
Self-host + complex logic	n8n (default 2026)
Stateful multi-step agent	LangGraph + Claude
Legacy desktop GUI 만	UiPath / Computer Use
Engineering team automation	Temporal + LLM step

기본값: n8n self-hosted + Claude tool-use node.

🔗 Graph

• 부모: Workflow-Automation
• Adjacent: Tool-Use · LangGraph

🤖 LLM 활용

언제: 매 unstructured input (email, PDF, chat), 매 fuzzy classification, 매 multi-step planning. 언제 X: 매 high-volume deterministic ETL — 매 SQL/Airflow 가 fast + cheap. 매 LLM call 의 매 step 의 cost overrun.

❌ 안티패턴

• LLM in tight loop: 매 step 의 매 LLM call — 매 latency + cost. 매 batch / cache.
• No HITL on irreversible: 매 send email / charge card 의 human approval gate 의 필수.
• Schema-less tool output: 매 free-text 의 parse error. 매 JSON schema enforce.
• Hidden non-determinism: 매 prompt 의 minor change 의 production 의 break. 매 eval suite 의 필요.

🧪 검증 / 중복

• Verified (Anthropic agent docs, n8n.io, LangGraph docs).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — modern AI agent automation 의 full content.