AI[SYS_ID: MULTI-AGENT-CUSTOMER-OPERATIONS-PLATFORM]

Multi-Agent Operations Platform

Multi-agent system where specialized agents handle different operational requests through routing, shared state and tool execution.

01. Overview

Multi-agent system where specialized agents handle different operational requests through routing, shared state and tool execution.

02. Interface / Screenshots

Multi-Agent Operations Platform
Multi-Agent Operations Platform project preview
[VISUAL_PLACEHOLDER]

No dedicated project screenshot is currently in the repository; this preview uses the shared visual placeholder.

03. Architecture

Interactive Architecture Walkthrough — a visualization of the implemented architecture/workflow.

Interactive Architecture Walkthrough

A routed workflow where specialist agents share state and use controlled tools to complete operational requests. [Visualization of the implemented agent architecture.]

Speed:
Next.js Interface
FastAPI Service
Request Router
Specialist Agents
Shared Workflow State
Controlled Tool Calls
PostgreSQL Audit Records
Coordinated Response
Active Pipeline Stage
Launch simulation to view stage details.
Transition Example
Start the walkthrough to view a sample transition.
Console Logs
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>Ready to trace workflow pipelines...

04. Problem

Customer operations often need different specialist agents for support, ordering, and reporting, but shared context is easy to lose.

05. Solution

Built a routed agent platform with shared state, structured tool calls, and PostgreSQL persistence so requests stay traceable across agent hops.

Backend focus

  • -FastAPI service layer for routing and tool execution
  • -PostgreSQL persistence for traceability and audit logs

AI focus

  • -LangGraph routing and orchestration
  • -LangChain-based tool execution
  • -Traceable multi-agent state management

06. Key Features

  • >Next.js interface for customer requests
  • >Support, order, and analytics agent routing
  • >Shared state across agent turns
  • >Structured tool calls
  • >Retries and audit logs
  • >Context preservation across agents

07. Engineering Challenges

Preserving context across multiple agents

Used shared state and audit logs to keep request history consistent.

Making routing behavior reliable

Added retries and structured tool calls around each agent step.

08. Outcome

Created a structured agent architecture where different workflows can be handled by specialized agents while remaining part of one coordinated system.

09. Technology

Next.jsFastAPILangGraphLangChainPostgreSQL
[PROJECT_SPECIFICATIONS]
ROLE:
Backend & AI Engineer
CATEGORY:
AI
TECHNOLOGY STACK:
Next.jsFastAPILangGraphLangChainPostgreSQL
OUTCOME:
Created a structured agent architecture where different workflows can be handled by specialized agents while remaining part of one coordinated system.