I built an AI multi-agent system from scratch. This is everything I learned — architecture decisions, real failures, and the patterns that actually work.
Not another chatbot wrapper. OpenClaw is an orchestration layer that lets multiple AI agents collaborate, share context, and execute complex workflows autonomously.
Autonomous AI workers with specific roles — planner, coder, reviewer, debugger. Each has its own context, tools, and decision-making loop.
The central router. Receives requests, determines which agents to invoke, manages authentication, rate limiting, and request routing.
Reusable capabilities agents can invoke — code execution, web search, file manipulation, API calls. Modular and composable by design.
Persistent context that survives across sessions. Agents remember past decisions, user preferences, and project state. Not just chat history.
Scheduled tasks, event triggers, and pipeline orchestration. Your agents work while you sleep — monitoring, deploying, reporting.
This isn't a theoretical diagram. This is my actual production setup — the machines, the routing, the failure points I discovered the hard way.
Building this system wasn't linear. Here's the real timeline — including the failures that taught me the most.
Setting up the gateway, configuring authentication, and getting the first agent to respond. Sounds simple. Took 3 days.
Gateway timeouts, agents losing context mid-task, memory leaks after 50+ requests. The default config was not production-ready.
Learned to read agent logs, trace request flows, and identify bottlenecks. The gateway's default timeout was 30s — most agent workflows need 120s+.
Set up automated health checks, log rotation, and scheduled agent tasks. The system started running itself.
After 6 weeks: 99.2% uptime, 4 active agents, automated monitoring, zero manual restarts. The system I actually wanted to build.
These aren't hypothetical. Every one of these crashed my system at least once. Here's how I diagnosed and fixed them.
ETIMEDOUT errors. Agent logs showed tasks completing after gateway had already returned 504.Once the system is stable, these patterns take it from "working" to "powerful." Each one was discovered through production usage.
When planner delegates to coder, pass structured context — not raw text. Define a handoff schema that both agents validate.
handoff: { task, context, constraints, deadline }Three tiers: session (ephemeral), project (persistent), global (cross-project). Agents query the right tier based on task scope.
memory.query(scope: "project", key: "auth-pattern")Don't poll. Use webhook triggers and filesystem watchers. When a PR merges, the review agent fires automatically.
on: { event: "pr.merged", agent: "reviewer" }Log every agent invocation: duration, token usage, success rate. Identify which agents are slow and which tasks cost too much.
metrics: { p95: "4.2s", tokens: "12k", success: "96%" }Every connection between gateway, agents, and storage uses mutual TLS. No exceptions. Self-signed CA for internal traffic.
tls: { ca: "internal.pem", verify: true }Independent tasks run concurrently. Planner identifies dependencies and dispatches non-blocking agents in parallel batches.
dispatch: [agent1, agent2] | await: [agent3]OpenClaw isn't a tool you use once. It's a collaborator that grows with you. These principles shaped how I work with it every day.
Let agents make decisions. Review the outputs, not the process. Intervene only when the result is wrong, not when the approach is unfamiliar.
Every agent failure is training data. Log why it failed, adjust the prompt or constraints, and let it try again. Systems improve through iteration.
Small agents with clear boundaries beat one agent that tries to do everything. Composition creates resilience. Monoliths create single points of failure.
Short, actionable insights. Each one saved me at least an hour of debugging or made my workflow measurably better.
Complex reasoning chains take time. A timeout that's too aggressive kills more tasks than it protects.
Cron doesn't load .bashrc or .zshrc. Wrap every cron command in a script that sources the environment first.
Store prompts in git. Tag versions. When an agent regresses, you can diff the prompt changes that caused it.
One runaway agent can drain your budget. Per-agent limits with alerts prevent cost surprises.
The number one security mistake. Default installs often bind to 0.0.0.0. Check with netstat -tlnp after every install.
When agents pass work to each other, use JSON schemas. Free text loses context at every handoff boundary.
A simple agent that pings every endpoint and alerts on failure catches issues before users do.
Agent memory grows fast. Schedule weekly cleanup to remove stale context. Fresh memory = better decisions.