AgentForge AI — Multi-Agent Software Engineering System

Build complete software systems using coordinated AI agents instead of isolated LLM responses.

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AgentForge AI is an autonomous multi-agent system that builds complete software systems from a single prompt.

Instead of relying on a single LLM response, AgentForge simulates a real software engineering team using 8 specialized agents that collaborate across the entire development lifecycle.

Each agent is responsible for a specific role:

  • Architect → system design and data flow
  • Backend Engineer → service logic and structure
  • API Engineer → endpoints and routing
  • Reviewer → validation and improvements
  • Security Agent → vulnerability detection and protection
  • Debugger Agent → edge cases and consistency checks
  • Database Engineer → schema and storage design
  • DevOps Agent → deployment and scaling strategy

From a single input, the system produces a structured, production-style system blueprint.

What It Does

  • Converts a single prompt into a complete system design
  • Simulates real engineering workflows using multiple agents
  • Produces structured, production-style outputs
  • Demonstrates end-to-end system generation with live execution

Inspiration

Most AI tools generate isolated outputs.

We wanted to build a system that behaves like a real engineering team, where multiple agents collaborate to produce structured and reliable results instead of disconnected responses.

How We Built It

We used JAC to orchestrate a multi-agent pipeline where each agent represents a role in the software development lifecycle.

Backboard provides LLM-based reasoning for each agent.

A FastAPI backend executes the pipeline, ngrok exposes it publicly, and a Lovable-based UI allows users to trigger the system and view outputs.

The system processes a prompt through all agents sequentially, generating a complete system design.

Challenges

  • Coordinating multiple agents with shared context
  • Maintaining structured outputs across roles
  • Debugging multi-stage pipeline execution
  • Connecting backend execution with UI in real time

Accomplishments

  • Built a working 8-agent system pipeline
  • Achieved end-to-end system generation from a single prompt
  • Integrated JAC, Backboard, and live backend
  • Delivered a functional UI + API demo

What We Learned

  • Multi-agent systems produce more structured results than single LLMs
  • Orchestration is key for scalable AI workflows
  • Role separation improves reasoning quality

What’s Next

  • Add persistent memory across agents
  • Enable real code generation and deployment
  • Expand to full SDLC automation
  • Improve scalability and integrations

Built With

  • backboard
  • fastapi
  • insforge
  • jac
  • lovable
  • ngrok
  • python
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