Experimenting with OpenClaw: Running a Multi-Agent AI System

I’ve spent the past two weeks experimenting with agentic AI systems. Not the “chat with a PDF” kind — I’m talking about AI agents that have actual tool access, can run scheduled tasks, and operate semi-autonomously.

This post documents what I’ve learned running a multi-agent system built on OpenClaw, an open-source framework for running AI agents.

🤖 What is OpenClaw?

OpenClaw is an open-source framework that turns a language model from “helpful chatbot” into an actual assistant that can do things. It gives agents file system access, shell command execution, web browsing, API integrations, cron scheduling, and multi-channel messaging (Telegram, Discord, Signal, etc.).

Think of it as the infrastructure layer for agentic AI. You define agents with different roles, give them tools, and let them work.

🧠 Multi-Agent Architecture

The interesting part isn’t running one agent — it’s running several with specialised roles:

• 🔐 Security agent — runs automated scans and flags suspicious activity
• 🔭 Research agent — scans feeds, summarises papers, tracks industry developments
• 💻 Code agent — handles implementation tasks, builds features, runs tests
• 🧹 Maintenance agent — cleans old logs, manages backups, routine housekeeping

Each agent gets its own model and cost profile. Security tasks get a capable (expensive) model because mistakes matter. Routine cleanup gets a fast, cheap model. This keeps costs manageable while maintaining quality where it counts.

📝 Lessons Learned

Match Models to Tasks

Not everything needs the most capable model. A security audit needs careful reasoning; a log rotation doesn’t. Tiering models by task complexity cut my costs significantly without sacrificing quality where it matters.

Memory Needs Structure

AI agents accumulate knowledge in markdown files — conversation summaries, decisions, learned preferences. Over time, I built simple tools for confidence decay (old facts shouldn’t be trusted as much), integrity checking, and source tracking. Treat agent memory as something that needs maintenance, not a write-only log.

Automate the Boring Stuff

Small automations compound. A backup verification script saves 5 minutes. A weekly scanner saves 10. A log rotator saves disk space. Individually, none of these matter much. Together, they create a system that mostly maintains itself.

The AI Isn’t the Hard Part

The hardest part isn’t the AI — it’s the plumbing. Cron scheduling, error handling, model selection, cost tracking. The AI parts mostly work. The infrastructure around them is where things break.

💰 Cost Management

Running multiple AI agents sounds expensive. Here’s how I keep it reasonable:

• Model tiering — expensive models only for tasks that need them
• Subagent defaults — spawned subtasks inherit a cheaper model unless overridden
• Session isolation — cron jobs run in isolated sessions that get cleaned up, preventing context bloat
• Heartbeat batching — periodic health checks use minimal tokens

The compound effect matters: five agents running on well-chosen models costs less than one agent running an expensive model for everything.

⚡ What Surprised Me

🤖 Agents monitoring agents works well. Having a security agent that audits other agents catches problems I wouldn’t notice manually.

🧠 Small automations compound. A dozen 5-minute automations create a system that mostly runs itself.

🔧 The plumbing matters most. The AI parts work. The infrastructure around them is where effort goes.

🏁 Practical Takeaways

If you’re considering running a multi-agent system:

1. ✅ Start with one agent, add roles gradually. Don’t architect a six-agent system on day one.
2. 💰 Match models to tasks. Not everything needs the most capable model.
3. 🔍 Automate verification, not just execution. A backup that isn’t verified isn’t a backup.
4. ⚠️ Treat agent memory as something to maintain. Build structure from the start.
5. 🔧 Test your scheduled tasks. Silent failures are the worst kind.

The agentic AI space is moving fast. Frameworks like OpenClaw make it accessible to individual developers. The gap between “I have an API key” and “I have a useful autonomous system” is mostly engineering discipline — the same kind that makes any software reliable.