Multi-Agent Platform - Case Study

The Problem

Single AI agents are limited. A chatbot can answer questions, but it can't do anything. An analysis tool can process data, but it can't decide what to analyze next. Complex workflows need coordination—multiple specialized agents working together toward a goal.

Existing multi-agent frameworks are powerful but complex. Building production-grade systems requires:

Agent orchestration and communication
Authentication and authorization
Real-time progress updates
State management and persistence
Rate limiting and resource management

Most developers starting with multi-agent systems spend more time on infrastructure than on agent logic. I wanted a platform that handles the plumbing so agents can be the focus.

The Solution

I built a full-stack demo platform that serves as both a reference architecture and a functional orchestrator for multi-agent systems. It provides the foundation for running agents in production, not just in notebooks.

Agent Registry: Dynamic registration and discovery of agent capabilities—agents declare what they can do, and the platform routes requests accordingly.

Orchestration Engine: Coordinates multi-step workflows across agents with state management, error handling, and retry logic.

Real-time Updates: Server-Sent Events (SSE) deliver sub-second progress updates to the frontend—users see agents working in real-time.

Authentication: JWT-based auth with role-based access control—different agents can have different permission scopes.

Persistence Layer: PostgreSQL stores run history, agent states, and workflow results—enabling pause/resume and post-mortem analysis.

Rate Limiting: Built-in rate limiting per user and per endpoint—preventing runaway agent loops and managing API costs.

Observability & Remote Operations

Multi-agent systems are complex by nature—multiple independent processes need coordination, and failures can occur anywhere in the chain. This platform is built for operations:

Run history persistence: Every agent execution is logged to PostgreSQL with full context—inputs, outputs, errors, and timing data for post-mortem analysis
Real-time progress streaming: SSE delivers agent status updates in sub-second intervals—watch agents work live, identify bottlenecks, and detect stuck workflows
Remote orchestration: Start, pause, resume, and cancel agent runs from anywhere—no server access required
Health endpoints: Each service exposes /healthz and /readyz endpoints—integrate with monitoring systems and alert on failures
Agent telemetry: Track which agents are called most frequently, average execution times, and failure rates—optimize your agent ecosystem based on real data

This observability transforms multi-agent systems from opaque black boxes into understandable, debuggable production services. You can trace every decision, retry every failure, and optimize based on actual usage patterns.

Architecture

The platform uses a polyglot architecture optimized for both performance and developer experience:

Frontend: React + TypeScript with TanStack Query for state management
Backend (API): Go for high-performance concurrent request handling
Backend (Agents): Python for AI/ML workloads (LangChain, LLM integrations)
Database: PostgreSQL with Drizzle ORM
Real-time: Server-Sent Events for progress streaming
Auth: JWT tokens with Redis-backed session management

Why this split? Go handles HTTP requests and concurrent operations efficiently. Python provides the rich AI/ML ecosystem. React delivers a responsive UI. Each language is used for what it's best at.

Go Python React TypeScript PostgreSQL Redis SSE JWT

The Result

What Built

A production-ready foundation for multi-agent systems with sub-second SSE updates and concurrent workflow orchestration. The platform demonstrates that multi-agent systems can be built and deployed like any other software—with proper auth, persistence, and monitoring.

Key capabilities:

Sub-second updates: SSE delivers agent progress in real-time, enabling responsive UIs
Concurrent workflows: Multiple agent runs execute simultaneously without interference
Fault tolerance: Failed agent tasks don't crash the workflow—errors are logged and can be retried
State persistence: Runs can be paused, resumed, and inspected post-execution
Production-grade auth: JWT-based authentication with proper token lifecycle management

What This Means for Clients

Multi-agent systems are the future of automation. But most organizations struggle to move beyond prototypes because the infrastructure gap is too wide. This platform demonstrates that the plumbing is solvable—and that multi-agent systems can be deployed like any other software.

Use cases for multi-agent automation:

Research + synthesis: One agent gathers information, another summarizes findings, a third formats reports
Approval workflows: Draft generation → review → revision → final approval with different agents at each stage
Data processing pipelines: Extraction → validation → transformation → loading with monitoring and error handling
Customer service: Triage agent → specialist agents → resolution agent with human oversight

The pattern is the same: break complex workflows into specialized steps, coordinate execution, and provide visibility throughout.

Get in Touch

Exploring multi-agent automation for your business? I design and build systems like this. Get in touch to discuss your use case.