AI Infrastructure

CrucibleAI – Distributed Multi-Agent Platform

Role: Systems Architect   //   Timeline: 2026

CrucibleAI – Distributed Multi-Agent Platform architecture

Core Metrics

  • Fully decoupled API routing from long-running LLM execution
  • Comprehensive telemetry tracing across all microservices

Technology Stack

Languages

Go, Python

Orchestration

gRPC, RabbitMQ, Celery, Docker Compose

Data Layer

PostgreSQL, Redis, Qdrant

Frontend

REST APIs

The Problem & Constraint

A production-oriented microservices backend for multi-agent AI workflows, decoupling API gateways from asynchronous Python execution engines to ensure high availability and observability.

System Topology

Separates the system into a Go API gateway, a Python-based AI engine, and asynchronous Celery workers. Components communicate through gRPC and RabbitMQ. Features domain models, persona-specific LangGraph agents, document processing pipelines, and vector search. Deployed via a Docker Compose stack configured with persistent volumes and local service orchestration.

Engineering Tradeoffs

  • 01

    Configured full-stack observability utilizing OpenTelemetry, Prometheus, and Grafana to track trace latencies across distributed AI agents.

  • 02

    Utilized asynchronous Celery workers and RabbitMQ to offload intensive LangGraph processing, preventing API gateway blocking.