[G1-Sync] Manual knowledge update
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Antigravity Agent
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---
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id: ai-code-agent-patterns
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title: Code Agent — Devin / Cursor / Claude Code
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category: Coding
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status: draft
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source_trust_level: B
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verification_status: conceptual
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created_at: 2026-05-09
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updated_at: 2026-05-09
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tags: [ai, agent, code, vibe-coding]
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tech_stack: { language: "TS / Python", applicable_to: ["AI"] }
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applied_in: []
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aliases: [Devin, Cursor, Claude Code, code agent, autonomous coding, SWE-bench, Aider]
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---
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# Code Agent
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> AI 가 code 작성 / 수정. **Cursor (IDE), Claude Code (CLI), Devin (autonomous), Aider (open)**. SWE-bench 가 eval.
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## 📖 핵심 개념
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- File read / edit / run tools.
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- Plan + execute + verify.
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- Sandbox (E2B / Daytona).
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- Test 가 ground truth.
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## 💻 코드 패턴
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### Aider (open source CLI)
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```bash
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pip install aider-chat
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cd my-repo
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aider --model claude-opus-4-7
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# 명령
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> add a /health endpoint to server.ts
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# → 자동 edit + commit.
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```
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→ Git-aware. 매 변경 = commit.
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### Cursor (IDE)
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```
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- Composer (multi-file).
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- Cmd-K (inline).
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- Tab autocomplete.
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- @file / @docs / @web reference.
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```
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→ VS Code fork.
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### Claude Code (CLI)
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```bash
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claude
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# → terminal-based agent.
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```
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→ Anthropic 의 native.
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### Tool composition
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```
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- read_file
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- write_file
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- edit_file (precise diff)
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- bash (run command)
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- search (grep / file)
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- web (fetch)
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```
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### Plan → Execute
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```
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1. User: "Add OAuth login".
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2. Plan: 5 step (read existing auth, add provider, route, ...).
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3. Execute: 매 step → read / edit / test.
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4. Verify: tests pass?
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5. Commit.
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```
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### Test-driven
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```
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1. Read existing test.
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2. Add new test (RED).
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3. Implement (GREEN).
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4. Refactor.
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5. Verify (re-run).
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```
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→ TDD with AI.
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### File reading strategy
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```
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- 직접 path: fast.
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- Search (grep): fuzzy.
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- Symbol search: function / class.
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- Recent change: git log.
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→ Context 가 limited. 정확 retrieve.
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```
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### Edit precision
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```
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Two strategies:
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1. Full file rewrite: 큰 file = waste.
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2. Diff / patch: 정확 + cheap.
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→ Aider / Claude Code 가 diff.
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Cursor 가 mix.
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```
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### Sandbox execution
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```
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- Test run (E2B / Modal).
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- Build verify.
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- Lint / type check.
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→ Real verification.
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```
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### SWE-bench
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```
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- 2294 real GitHub issue.
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- "Fix this issue" given repo + issue.
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- Pass = test 가 새 + 옛 둘 다 OK.
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→ State-of-the-art:
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- 2023: 2-3% pass.
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- 2024: 30-50% pass.
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- 2026: 60%+ pass.
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```
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→ 매년 ↑.
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### Devin (autonomous)
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```
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- 큰 task 가 hours.
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- Browser + code + plan.
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- 사람 review 가 필요.
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```
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→ "AI software engineer".
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### Limitations
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```
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- Long context (큰 codebase) = lost.
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- 매우 새 / niche 기술.
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- Mathematical / algorithmic.
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- Multi-step refactor.
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- Production debugging (real-time).
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```
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→ 사람 가 still 필요.
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### Best practice (사용자)
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```
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- Small task: agent.
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- Big task: human plan + agent execute.
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- Critical / security: human review.
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- Test 가 baseline.
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- Git commit 가 자주 (rollback).
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```
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### Prompt 가이드
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```
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✓ "Fix bug in users.ts:42 — null check 가 missing for empty list".
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✗ "Make better".
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✓ "Add /health endpoint returning {status: 'ok'}".
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✗ "Add monitoring".
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→ Specific. 명확.
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```
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### Multi-file edit
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```
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"Refactor all UserService.* call to NewUserService".
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→ Agent 가:
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1. Search 모든 caller.
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2. 매 file 의 edit.
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3. Run test.
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4. Iterate.
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```
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### Code review by AI
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```
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PR review:
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- Read diff.
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- Comment specific.
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- Suggest improvement.
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- Find bug.
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→ CodeRabbit / Greptile / Sourcery.
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```
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### Production agent
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```
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- Agentic IDE (Cursor, Windsurf, Zed AI).
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- CLI (Claude Code, Aider).
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- Autonomous (Devin, Cognition).
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- PR review (CodeRabbit).
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- Inline (Copilot, Tabnine).
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```
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### MCP (Model Context Protocol)
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```ts
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// Anthropic 의 표준.
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// Editor 가 server expose: file, git, run.
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// Agent 가 같은 protocol.
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```
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→ Cursor / Claude Desktop / Cline 가 native.
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→ [[AI_MCP_Server_Building]].
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### Eval
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```
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Internal:
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- Unit test pass rate.
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- Refactor preservation.
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- Code style 일관.
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Public:
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- SWE-bench, HumanEval, BigCodeBench.
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```
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### Multi-agent (subagent)
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```
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Main agent:
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- Plan + coordinate.
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Sub-agent:
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- Search file.
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- Run test.
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- Refactor specific.
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→ Anthropic 의 Claude Code / multi-agent.
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```
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### Memory
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```
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- Conversation context.
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- File contents.
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- Git history.
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- Test results.
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→ Memory budget. Compress.
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```
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### 함정
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```
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- Agent 가 infinite loop.
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- Cost 폭발 (큰 task = 매 hour $).
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- Test 없음 = silent break.
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- Git 없음 = rollback 불가.
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- Human review 없음 = production bug.
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```
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### Workflow integration
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```
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1. GitHub issue → agent.
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2. PR open → agent review.
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3. Test fail → agent fix.
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4. Human approve → merge.
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→ AI + human loop.
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```
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### Privacy
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```
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- 코드 가 model API server.
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- Local model (Ollama) = privacy.
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- Code-only fine-tune (CodeLlama).
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→ Sensitive code = local / private cloud.
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```
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### Cost
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```
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1 day Claude Code: $5-50 (tokens).
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Cursor Pro: $20 / month.
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Devin: $500 / month.
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Aider + own API: $10-100 / month.
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→ Productivity ↑ 가 cost 정당화.
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```
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### Future
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```
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- 점점 autonomous.
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- Test-first AI.
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- Long-context (1M+ token).
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- Multi-modal (UI screenshot → code).
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- Fine-tuned per-codebase.
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```
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## 🤔 의사결정 기준
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| 작업 | 추천 |
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| Quick edit | Cursor / Copilot |
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| Multi-file | Cursor Composer / Claude Code |
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| Autonomous | Devin (review 필요) |
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| Open / CLI | Aider |
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| PR review | CodeRabbit / Greptile |
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| Test write | Any agent + test |
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| Refactor | Aider / Cursor |
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## ❌ 안티패턴
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- **Agent 만 + no review**: production bug.
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- **No test**: silent break.
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- **No git commit**: rollback X.
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- **Vague prompt**: bad output.
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- **Long-running 무 supervision**: cost / loop.
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- **Sensitive code 가 public model**: leak.
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## 🤖 LLM 활용 힌트
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- Cursor / Claude Code 가 modern default.
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- Test = ground truth.
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- Aider 가 git-aware open.
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- MCP 가 표준 protocol.
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## 🔗 관련 문서
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- [[AI_Tool_Composition_Deep]]
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- [[AI_Multi_Agent_Coordination]]
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- [[AI_Agent_Sandbox_E2B]]
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