Skip to content

TEST_RESULTS.md

Latest commit

 

History

History
106 lines (80 loc) · 2.67 KB

TEST_RESULTS.md

File metadata and controls

106 lines (80 loc) · 2.67 KB

Test Results - AI Engineering Team

Test 1: Todo List REST API

Prompt: "build me a simple REST API for a todo list with create, read, update, and delete operations"

Status: ✓ APPROVED

Pipeline Execution:

  • Analyst: 700 tokens (13s)
  • Architect: 1,331 tokens (15s)
  • Coder: 2,318 tokens (15s)
  • QA: 2,761 tokens (8s)
  • Lead Architect: 3,771 tokens (8s)

Total Time: ~59 seconds Total Tokens: 10,881 tokens

Deliverable: Complete Flask-based REST API with all CRUD operations, including:

  • Data models (TodoItem class)
  • Routes for Create, Read, Update, Delete
  • In-memory storage implementation
  • Error handling
  • API documentation outline

Test 2: Real-Time Chat Application Architecture

Prompt: "design a real-time chat application architecture with user authentication and message persistence"

Status: ✓ APPROVED

Pipeline Execution:

  • Analyst: 801 tokens (12s)
  • Architect: 1,636 tokens (13s)
  • Coder: 2,496 tokens (12s)
  • QA: 3,031 tokens (10s)
  • Lead Architect: 4,424 tokens (14s)

Total Time: ~61 seconds Total Tokens: 12,388 tokens

Deliverable: Comprehensive architecture design including:

  • Complete system architecture (Client/Server/Database)
  • Full file/folder structure
  • Data models (User, Message, Chat)
  • WebSocket implementation plan
  • Authentication service with JWT
  • Message persistence strategy
  • Scalability considerations

System Performance

Strengths

  1. Multi-agent collaboration works flawlessly
  2. Each agent provides specialized perspective
  3. QA catches inconsistencies effectively
  4. Lead Architect provides thoughtful final review
  5. Output is comprehensive and actionable

Observations

  1. Token usage increases as context builds (expected)
  2. Each agent execution takes 8-15 seconds
  3. Total pipeline time: ~1 minute per request
  4. All agents currently using GPT-4o (works great)

Agent Contributions

Analyst:

  • Breaks down requirements clearly
  • Identifies missing information
  • Defines acceptance criteria

Architect:

  • Provides system design
  • Defines file structure
  • Sets MVP boundaries

Coder:

  • Creates implementation plans
  • Provides code sketches
  • Flags technical considerations

QA:

  • Reviews all outputs for consistency
  • Identifies conflicts
  • Rates MVP viability

Lead Architect:

  • Makes final decision
  • Provides reasoning
  • Compiles deliverable or revision instructions

Configuration Notes

Currently configured with all agents using OpenAI GPT-4o.

For Claude models, valid model names include:

  • claude-3-opus-20240229
  • claude-3-sonnet-20240229
  • claude-3-haiku-20240307

Note: Claude 3.5 Sonnet availability may vary by API access level.