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

• https://lablab.ai/event/ai-agents-hack-with-lablab-and-mindsdb/the-synapses

Colab Notebooks

• https://colab.research.google.com/drive/1vsYzaJSXERfU-uEXuK2aD4HA9QbEIVUl?usp=sharing
• https://colab.research.google.com/drive/1j9OTh4ridFnq6XQReiYcItzG9yi3Ydwh?usp=sharing

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

• Developing a Multi-Agent System with CrewAI, https://lablab.ai/t/crewai-multi-agent-system,
• Mastering AI Agent Management with AgentOps: An In-Depth Guide, https://lablab.ai/t/agentops-tutorial