Our AI Setup: A Multi-Agent System for Autonomous Operations
We run a multi-agent AI system called OpenClaw. This post explains what it is, how it works, and why we built it this way.
What is OpenClaw?
OpenClaw is our internal AI infrastructure. It is not a single AI assistant – it is a system of multiple AI agents, each with specific roles, working together autonomously. Think of it as a digital team rather than a digital employee.
The core problem we solved: traditional AI assistants forget everything between conversations, cannot handle multiple complex tasks simultaneously, and degrade in quality over time. OpenClaw addresses these through specialized agents with persistent memory.
The Agents
Our system currently runs several agents:
Iron Man – Infrastructure coordinator. Handles server management, deployments, monitoring. Keeps the system running.
Deadpool – Task execution agent. Handles hands-on work like playing poker, testing strategies, running automated tasks.
Nite Owl – Content and research. Handles writing, analysis, information gathering.
Each agent runs in its own isolated container with dedicated resources. They do not compete for memory or processing power.
How Memory Works
The most important part of the system is memory. We use a three-layer approach:
Session Memory – Each conversation starts fresh. The agent loads its identity, user preferences, and current context. This is short-term working memory.
Daily Logs – Every significant event gets recorded to daily log files. This includes completed tasks, decisions made, errors encountered, and results achieved. The agent can reference these to understand what happened recently.
Long-term Memory – Important learnings get distilled into a semantic memory system. This is searched when context requires historical information. Not everything gets saved – only what matters.
This three-layer system prevents the common AI problem of context collapse, where the assistant forgets important context or becomes confused after extended use.
Communication Between Agents
Agents coordinate through shared memory files and a messaging system. When one agent completes a task, it updates shared state. Other agents can read this to understand what happened.
We also have plans for a Matrix client for group chat coordination, though this is still in development.
Technical Details
The system runs on Proxmox VE, a virtualization platform. Each agent runs in a Linux Container (LXC) – lightweight virtual machines that provide isolation without the overhead of full virtualization.
Host machine specs: HP EliteDesk 800 G2, running Proxmox VE 8.3. This handles multiple containers comfortably.
For AI capabilities, we use OpenRouter as an aggregation layer. This gives access to multiple AI providers (Google Gemini, Kimi, Grok, Minimax) through a single API. We can switch models based on task requirements.
What Can It Do?
Currently, our agents handle:
- Content creation and research
- Automated testing and task execution
- System monitoring and maintenance
- Information synthesis and analysis
The system is designed to be extendable. New agents can be added for new domains.
Challenges We Faced
Building this was not straightforward. Some challenges:
Verification – AI systems sometimes report success before verifying actual results. We implemented strict verification protocols – three independent checks before reporting any external outcome.
Memory Management – Deciding what to remember and what to discard required careful thought. Too much memory creates noise; too little loses important context.
Coordination – Multiple agents need to avoid conflicts and communicate effectively. We use shared files and structured communication protocols.
Reliability – The system must run consistently. We monitor for errors and have recovery procedures.
Lessons Learned
If you are building something similar:
Start simple – Do not try to build a complex multi-agent system immediately. Begin with one agent, prove it works, then add complexity.
Verification is critical – Never trust an AI reporting its own success without independent verification.
Memory architecture matters – How you handle memory determines system capability. Invest time in designing this properly.
Isolation prevents problems – Running each agent in its own container prevents resource conflicts and contains failures.
Current Status
The system is operational and handling real tasks. We continue to refine and improve it. The agents are working autonomously, with human oversight for important decisions.
This setup has significantly improved our operational capacity. Tasks that previously required constant attention now run automatically.
Future Plans
We intend to expand the agent army with more specialized roles. Plans include:
- Additional research agents
- More comprehensive monitoring
- Enhanced inter-agent communication
- Broader task automation
Conclusion
Multi-agent AI systems offer significant advantages over single-assistant approaches. With proper memory architecture, isolation, and verification, they can handle complex, long-running operations reliably.
This post explained our specific implementation. Every setup will be different based on requirements, but the principles apply broadly.