Case Study: Multi-Agent AI System

📌 Challenge

The enterprise needed an AI system handling diverse tasks — infrastructure monitoring, software development, knowledge management — without human intervention. Key issues:

Operations workload too large for a small team
Need for rapid response to security and infrastructure incidents
Daily reporting from multiple sources was time-consuming
Desire to automate software development and deployment processes

🛠 Solution

Manager Agent (aAI)

Central orchestrator. Receives requests, analyzes context, delegates to specialist agents, synthesizes results. Smart intent routing using keywords + LLM classification.

Infra Agent (aAI)

Infrastructure specialist. Queries Wazuh for vulnerabilities, Zabbix for system metrics. Auto-analyzes logs, detects anomalies, generates security reports with remediation recommendations.

Coding Agent

Software development specialist. Receives feature requests, analyzes codebase, writes code, runs tests, creates pull requests. Supports debugging, code review, auto-deploy.

aAI Gateway

Platform connecting all agents. Provides TaskFlow orchestration, session management, tool registry, multi-model support. Durable execution with auto-retry.

🏆 Results

✓
24/7 operation — not dependent on human working hours
✓
Automatic issue detection for security and infrastructure in real time
✓
Daily comprehensive reports — no manual aggregation needed
✓
Software development support — reduced dev time with Coding Agent

💼 Technology Stack

aAI Gateway DeepSeek Python Node.js TaskFlow MCP Protocol Telegram Bot API Wazuh / Zabbix

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