Back to Architecture Deep-DiveFrom Manual Ops to
Case Study90-Day Production Data
From Manual Ops to
Autonomous Infrastructure
How a 22-agent AI fleet eliminated manual incident response, reduced infrastructure costs by 31%, and achieved sub-3-second mean time to resolution — running entirely on self-hosted hardware.
Measured Results
Incident Response
Mean Time to Resolve (MTTR)
3.2 hours< 3 seconds
-99.97%Manual Incidents per Month
120
-100%Alert Classification Accuracy
78%94%
+16ppOperational Efficiency
Ops Engineering Time Saved
Baseline (100%)53% of baseline
-47%Infrastructure Cost
Baseline (100%)69% of baseline
-31%Escalation Rate
23%8%
-65%Implementation Timeline
Phase 1 — Foundation
Weeks 1–2- Sentinel Agent + LangGraph ReAct loop
- Infrastructure health monitoring (Docker, Proxmox)
- Circuit breaker pattern for fault isolation
Phase 2 — Intelligence Layer
Weeks 3–5- Orchestrator V2 with DAG workflow engine
- BI Agent with PostgreSQL-backed executive reports
- RAG knowledge base + security scanning
Phase 3 — Autonomous Operations
Weeks 6–8- Management trio (PM, DM, PO) with persistent state
- Career pipeline: 5-agent job search automation
- Weekly executive briefing DAG
Phase 4 — Production Hardening
Weeks 9–12- 405 tests passing, 0 failures
- SLA persistence + incident tracking
- Fleet consolidation: 25 → 23 active agents
Technology Stack
LangGraphReAct agent loops + DAG workflow execution
Qwen 2.5:7bLocal LLM via Ollama — zero API cost
FastAPIAgent HTTP layer — all 22 agents
PostgreSQLSLA tracking, KPI history, management state
RedisWorkflow state persistence + crash recovery
Docker ComposeFleet orchestration — 30+ containers
Prometheus + GrafanaMetrics collection + visualization
Proxmox VEHypervisor — VMs + LXC containers
Fleet Architecture
22
Active Agents
6
DAG Templates
405
Tests Passing
0
Failures
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