Agent Rag Retrieval Spec.spec.prompt

---
id: 001
title: agent-rag-retrieval-spec
stage: spec
date: 2025-12-26
surface: agent
model: claude-sonnet-4-5-20250929
feature: agent-rag-retrieval
branch: 001-agent-rag-retrieval
user: Claude
command: sp.specify
labels: [spec, agent, rag, retrieval, openai]
links:
  spec: specs/001-agent-rag-retrieval/spec.md
  ticket: null
  adr: null
  pr: null
files:
 - specs/001-agent-rag-retrieval/spec.md
 - specs/001-agent-rag-retrieval/checklists/requirements.md
tests:
 - specs/001-agent-rag-retrieval/spec.md validates feature requirements for AI agent with RAG capabilities
---

## Prompt

Build an Ai agent with retrieval-augmented capabilities

target audience: Developers building agent-based RAG system
focus: agent archestration with tool based retrieval over book content
success criteria:
- agent is created using the openAI Agents SDK
- Retrieval tool successfully queries qdrant via spec-2 logic
- Agent answers questions using retrieval chucks only
- Agent can handle simple follow-up queries

constraints:
- tech stack: python, openAI agent SDK, Qdrant
- Retrieval: reuse existing retrieval pipeline
- format: minimal, modular agent setup
- timeline:  complete within 2-3 tasks

not building:
-  frontend or UI
- fastAPI integration
- authentication or user sessions
- model fine-tuning or prompt experimentation

## Response snapshot

Created a comprehensive specification for an AI agent with retrieval-augmented capabilities. The specification includes:

- Updated feature name and branch information
- Constitutional alignment addressing technical accuracy, clarity, reproducibility, and standards
- Three prioritized user stories covering agent creation, retrieval integration, and follow-up queries
- Seven functional requirements detailing the system capabilities
- Four key entities identified (AI Agent, Retrieval Tool, Book Content, Query)
- Six measurable success criteria for the feature

The specification follows the template structure and addresses all mandatory sections. A quality checklist was also created to validate the specification completeness.

## Outcome

- ✅ Impact: Created complete feature specification for AI agent with RAG capabilities
- 🧪 Tests: Specification includes testable user stories and acceptance criteria
- 📁 Files: Created/updated spec.md and requirements.md files
- 🔁 Next prompts: Ready for planning phase with `/sp.plan`
- 🧠 Reflection: Specification addresses all required sections and constraints

## Evaluation notes (flywheel)

- Failure modes observed: None
- Graders run and results (PASS/FAIL): N/A
- Prompt variant (if applicable): N/A
- Next experiment (smallest change to try): Proceed to planning phase