Skip to content

Latest commit

 

History

History
62 lines (49 loc) · 2.72 KB

README.md

File metadata and controls

62 lines (49 loc) · 2.72 KB
Raw

AI-Agents

Lablab AI Agents Hackathon, 13-15th Sept, 2024

Aim: Build an agentic workflow for the software development life cycle (SDLC)

Approach

  • Build a PoC in a sandbox (dev) environment
  • Break the workflow into four separate tasks, and use one agent per task
  • Use crew.ai for the agents and llama 3.1 or Gemma-2-7b for the llms
  • Use colab notebooks for the coding
  • Use other tools as necessary, e.g., agentops, autogen, mindsdb, upstage, langgraph, composio, etc.

Agents

  • Requirements Agent: Understand requirements from a given requirements doc
  • Design Agent: Create a high level design doc
  • Software Development Agent: Generate codebase to build a PoC (small project)
  • Code Test Agent: Generate code tests

Other ideas: Agents for the MLOps life cycle

Workflow Steps

a. Requirements Gathering

  • Task: Extract key requirements from a document
  • Goal: Create a concise summary of the system's required features
  • Outcome: Define the project scope (e.g., authentication, operations, task management, reporting)

b. High-Level System Design

  • Task: Design the architecture of the system
  • Diagrams Generated:
    • Use Case Diagram
    • Class Diagram
    • Entity-Relationship Diagram (ERD)
    • UI/UX Design for Dashboard
  • Outcome: A document detailing the description, architecture and components of the system. It will include text and diagrams.

c. Code Generation

  • Task: Develop the Code for the system
  • Goal: Create functional code that implements core features
  • Outcome: Working code implementing the basic system including authentication, database operations, and reporting

d. Code Testing

  • Task: Run test cases to verify code functionality
  • Goal: Ensure the system meets the requirements and works as expected
  • Outcome: A detailed test report highlighting results and potential issues

Project Page

Colab Notebooks

Future Work

  • Improvements in Design Diagrams: Explore more AI-driven tools for automated generation of detailed design diagrams
  • Customization: Enable more advanced configurations for tasks such as adding new agents or expanding the functionality
  • Agent Testing: Add test, debug and monitoring features to our platform, such as AgentOps.ai
  • Deployment: Plan for deployment of the final PoC in a production environment

References