AGENT FORGE

Build Faster with AI-Powered Agents

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Multi-Agent Development Platform

A contract-first, multi-agent AI development platform that orchestrates specialized AI agents to collaboratively build software through a structured pipeline.

Core Concept

Instead of a single AI writing all code, this platform coordinates multiple specialized agents—each with distinct roles—working together under shared API contracts and security constraints.

Architecture Highlights

5-Phase Pipeline:

  1. Planning & Memory — Orchestrator decomposes requests and retrieves context from a RAG-powered memory layer (coding standards, past decisions, error patterns)
  2. Contract Negotiation — Python and JavaScript agents propose/validate API schemas, locked in a Schema Registry before implementation begins
  3. Implementation — Language-specific agents generate code and tests against the locked contract, while SecOps audits in parallel
  4. Smart Verification — Sandbox executes code; Verifier classifies errors by severity (auto-fix linting → return to agent → escalate to planner)
  5. Delivery — Integrator merges artifacts, signs releases, and updates memory for future tasks

How AgentForge Was Built

Tech Stack:

  • Frontend: React 18 + TypeScript + Vite for fast development
  • Styling: Tailwind CSS with semantic design tokens + shadcn/ui components
  • Animations: Framer Motion for smooth transitions
  • Code Editor: Monaco Editor (VS Code's engine) for in-browser editing
  • Live Preview: react-live for real-time React component rendering
  • Backend: Lovable Cloud (Supabase) for auth, database, and edge functions

Architecture Approach:

  1. Contract-First Design — Schema Registry locks API specs before agents implement
  2. Modular Components — Small, focused files (verification logic in src/lib/verification/, verifier UI in src/components/verifier/)
  3. Dockerized Sandbox — Isolated containers for executing untrusted Python/Node code with resource limits
  4. RAG Memory Layer — Context injection to maintain coding standards across agent outputs

Key Patterns:

  • Route-based code splitting with React.lazy
  • Custom hooks for collaboration, conversations, and favorites
  • Centralized type definitions for the Unified Task Schema
  • Real-time collaboration via Supabase Realtime channels

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Key Features

  • Agent Workspace with Monaco editor, live React preview, and chat interface
  • Unified Task Schema tracking status, contracts, security constraints, and subtasks
  • Real-time collaboration with live cursors and activity feeds
  • Isolated sandbox execution via Docker containers
  • Design token sync for exporting styles to CSS/Tailwind/Figma

Purpose

Reduce "spaghetti code" chaos by enforcing interface contracts before implementation, enabling multiple AI agents to work cohesively on complex projects with built-in security scanning and automated repair loops.

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