+ Concise, operations-focused essential questions.
- Omits runtime infrastructure and delivery timeline questions.
ROLE You are a senior architect of production-ready AI agents and a business process automation specialist.
| Category | Development › Deploy & operations |
|---|---|
| Tags | DraftingAnalyzingDeveloperChecklist |
ROLE
You are a senior architect of production-ready AI agents and a business process automation specialist.
TASK
Help design an AI agent for the process described below.
The agent must be reliable, controllable, token-efficient, and suitable for regular use.
CONTEXT
Process:
${process:Describe the current manual task in detail}
Expected output:
${expected_output:What should the agent produce?}
Data sources:
${data_sources:Websites, spreadsheets, CRM, Telegram, email, files}
Available tools:
${tools:APIs, MCP, scripts, browser, database}
Run frequency:
${frequency:Scheduled, event-triggered, or manual}
Constraints:
${constraints:Budget, time, API rate limits, security requirements}
Critical risks:
${risks:Data deletion, publishing, payments, access credentials}
---
WORKFLOW
First, ask any clarifying questions that are essential for designing a reliable system.
After receiving answers, proceed through all 15 steps:
1. Break the process into discrete stages
2. Identify where LLM is needed vs. where a simple script is enough
3. Define input and output data for each stage
4. List all required tools, APIs, and access credentials
5. Propose a memory and state management structure
6. Design the main agent loop
7. Add result verification after each critical stage
8. Add error handling, retries, and fallback routes
9. Define stopping conditions and rate limits
10. Identify actions that require human approval
11. Propose a logging, metrics, and alerting system
12. Describe a safe self-improvement mechanism via error analysis
13. Create a list of test scenarios
14. Propose a project file structure
15. Prepare a step-by-step development plan
---
DELIVERABLES
Split the solution into three versions:
🟢 MVP — minimal working agent (fast to ship)
🟡 STABLE — reliable version for regular production use
🔵 PRO — advanced version with memory, monitoring, and self-improvement
Then output:
- System architecture overview
- Data flow diagram (text-based)
- Full tool and API list
- Pseudocode for the main loop
- Recommended folder structure
- Step-by-step development roadmap
- Security checklist
- Testing checklist
- Agent readiness criteriaThis is useful for designing a reusable business-process automation agent. It splits the solution into MVP, STABLE, and PRO versions and asks for verification, approvals, logging, security, and testing checklists.
Claude is the most complete and specific; ChatGPT is more concise but misses a few questions. Gemini violates the required clarification sequence, asserts unsupported costs, accuracy, and tools, and is truncated.
+ Concise, operations-focused essential questions.
- Omits runtime infrastructure and delivery timeline questions.
+ Clearly identifies key design variables and safe defaults.
- Somewhat lengthy for an initial clarification round.
+ Provides a highly concrete flow and implementation outline.
- Skips clarification and invents assumptions and metrics.
| Criterion | ChatGPT | Claude | Gemini | Leader |
|---|---|---|---|---|
| Instruction following | 10 | 10 | 2 | Tie |
| Accuracy | 9 | 9 | 3 | Tie |
| Specificity | 9 | 10 | 8 | Claude +11% |
| Structure | 9 | 9 | 6 | Tie |
| Right length | 10 | 8 | 2 | ChatGPT +25% |
Scored 1–10 by gpt-5.6-sol with model names hidden (2026-09-24). This is an AI review, not a measurement.
We gave three models the same input and copied their answers unedited. Each ran in its CLI (an agent harness), and answers in the ChatGPT or Claude apps or on the web may differ. Outputs are in Korean.
[Pasted material] process: 고객센터에 접수되는 CS 티켓을 담당자가 수작업으로 읽고 카테고리(배송/환불/제품불량/기타)를 분류한 뒤 담당 부서에 수동으로 전달하고 있음. 하루 평균 200건, 분류에만 2명이 3시간씩 씀. expected_output: 티켓별 카테고리 태그, 우선순위(긴급/보통/낮음), 담당 부서 배정 결과를 CRM에 자동 기록 data_sources: Zendesk 티켓 API, 내부 고객 DB(PostgreSQL), 상품 카탈로그 스프레드시트 tools: Zendesk API, Slack 알림 webhook, 사내 CRM REST API frequency: 신규 티켓 생성 시 이벤트 트리거 constraints: 월 예산 30만원 이내, 오분류 시 담당자가 5분 내 재배정 가능해야 함, 고객 개인정보는 외부로 전송 금지 risks: 잘못된 부서 배정으로 인한 응대 지연, 긴급 환불 건을 낮은 우선순위로 오분류할 위험
신뢰성 있는 설계를 위해 아래 사항을 먼저 확인해야 합니다.
분류 기준
우선순위 기준
부서 배정
개인정보 및 AI 실행 환경
CRM 연동
이벤트 및 재처리
정확도와 운영 기준
답변이 없는 항목은 안전한 기본값을 적용해 설계할 수 있습니다. 기본값은 단일 카테고리, 보수적인 긴급 판정, 개인정보 마스킹, 낮은 신뢰도 자동 보류, 자동 재시도와 담당자 재배정 가능 구조로 두겠습니다.