Case Study

AI Agent Orchestrator

Multi-Agent System for Autonomous Task Execution

The Problem

The Challenge

Single LLM calls struggle with complex, multi-step tasks requiring research, coding, and verification. Users need systems that can break down goals and execute them autonomously.

The Solution

How I Solved It

Designed a multi-agent architecture with an Orchestrator agent that delegates to specialized Workers (Research, Coding, Review). Implemented MCP for tool discovery and a Kanban-style state machine for task tracking.

Outcome

Result & Impact

Autonomous system capable of handling complex development tasks, reducing manual coding time by 60% for repetitive workflows.

Key Highlights

Technical Achievements

  1. Multi-agent decomposition & coordination
  2. Tool-use execution with MCP integration
  3. Persistent memory & RAG for context
  4. Human-in-the-loop approval workflow
Tech Stack

Tools Used

TypeScriptLLM APIsCrewAIMCPVector SearchHermes Framework