Case study 01 — Multi-agent AI operations system
LiveThe problem
Manual operations and repetitive system tasks often lead to human errors and system downtime. Relying on a single, general-purpose AI assistant to draft, verify and execute operational updates is fragile because a single assistant cannot reliably check its own output.
What I built
I built a multi-agent operational environment that divides complex administration tasks across separate, role-specific agents. The system coordinates actions through structured channels and state files, ensuring every output undergoes independent verification.
How it works
- Role-specific agents run under distinct profiles with instructions declared in external file-based rules rather than conversational memory.
- Agents collaborate and exchange structured messages through dedicated, named channels that record a complete execution audit.
- Independent adversarial roles separate drafting from checking, where one specialist agent actively reviews and validates the output of another.
- The environment executes workflows across multiple LLM providers so the system is not tied to a single model provider.
Where the human stays in control
The workflow halts and requests explicit human approval before any critical state modification or file-system update is executed. This gate ensures that automated agents never make irreversible actions without oversight.
What it produces
The system generates structured operational records, capturing 103 incident and learning files, each in its own file, between June and September 2026. It establishes more than 160 reusable packaged workflows and automates environment checks using more than 50 hooks.
Stack
Claude Code · MCP · Hermes profiles · file-based rules