Using the OpenClaw Agent Command for AI-Powered Messaging

One of the things I like most about OpenClaw is that it treats AI agents as first-class citizens in your messaging infrastructure. The openclaw agent command is where that philosophy becomes practical - it's how you trigger agent turns, send messages, and route responses across channels, all from the command line.

If you've been setting up OpenClaw or evaluating it for your organisation, understanding how the agent command works is probably the most useful thing you can spend 10 minutes on. It's the entry point for most of the automation you'll build.

What the Agent Command Does

At its core, openclaw agent runs an agent turn through the OpenClaw Gateway. An "agent turn" is one cycle of an AI agent processing input, deciding what to do, and optionally delivering a response. You can target a specific agent, send it a message, specify the delivery channel, and control how much thinking the model does.

The basic pattern looks like this:

openclaw agent --to +15555550123 --message "status update" --deliver

That sends a message to a phone number through whatever channel is configured for that destination. Simple enough.

But the real power comes when you start using named agents and channel routing:

openclaw agent --agent ops --message "Summarise logs" --deliver --reply-channel slack --reply-to "#reports"

This tells your "ops" agent to summarise logs, then deliver the response to a Slack channel called #reports. The agent does the thinking, OpenClaw handles the routing. You can mix and match input sources and output destinations however you need.

Key Options and How to Use Them

Targeting agents. Use --agent <id> to run against a specific configured agent. If you've set up multiple agents in OpenClaw (one for customer service, one for internal ops, one for reporting), this is how you pick which one handles the request.

Session continuity. The --session-id flag lets you continue an existing conversation:

openclaw agent --session-id 1234 --message "Summarise inbox" --thinking medium

This matters when your agent needs context from previous messages. Without a session ID, each turn starts fresh.

Thinking levels. The --thinking parameter controls how much reasoning the model applies. For quick lookups, you might not need deep thinking. For complex summarisation or decision-making, bump it up. This is a practical cost and latency control - not every message needs the model to think hard.

Delivery and routing. The --deliver flag tells OpenClaw to actually send the response, not just generate it. Without this flag, you get the response back in your terminal but nothing goes out. The --reply-channel and --reply-to flags control where the response lands.

Practical Use Cases

The command-line interface might seem like it's just for developers, but it's actually the foundation for all sorts of automation. Here are patterns we've seen work well:

Scheduled Reports

Pair the agent command with cron or any scheduler:

# Daily at 8am - generate and send a summary
openclaw agent --agent reporting --message "Generate daily summary" --deliver --reply-channel slack --reply-to "#daily-updates"

Your reporting agent pulls data, generates a summary, and posts it to Slack. No human in the loop, no dashboard to check, the information comes to you.

Event-Driven Responses

Trigger agent turns from webhooks, monitoring alerts, or CI/CD pipelines:

openclaw agent --agent ops --message "Build failed for service-api: ${BUILD_LOG}" --deliver

When something breaks, your ops agent can analyse the failure, suggest fixes, and notify the right people - all kicked off by a single command.

Interactive Workflows

For more complex scenarios, you can chain agent turns together, using the output of one as input to another. The session ID support means you can build multi-step workflows where context carries through.

A Note on Security

One thing worth calling out: when the agent command triggers models.json regeneration, any credentials managed through SecretRef are persisted as non-secret markers (like environment variable names), not as the actual secret values. This is a deliberate design choice. OpenClaw keeps your secrets resolved at runtime only, so they don't end up sitting in config files on disk.

If you're evaluating OpenClaw for an environment with strict compliance requirements, this is exactly the kind of detail your security team will care about. The markers are source-authoritative, meaning OpenClaw writes them from the active source config snapshot rather than from resolved runtime values.

Where This Fits in the Bigger Picture

The agent command is one piece of OpenClaw's architecture, but it's the piece you'll interact with most directly. It connects to the broader system of channel routing, agent configuration, and tool integrations that make OpenClaw useful for real business workflows.

For organisations already using OpenClaw, the agent command is how you'll automate most of your AI-powered messaging. For those evaluating it, spending time with this command gives you a quick feel for whether OpenClaw's approach matches how you think about AI agent deployment.

We've written about what OpenClaw is and how it works in more detail, and if you're interested in the broader channel routing capabilities, check out our piece on OpenClaw channel routing.

If you're looking at deploying AI agents across messaging channels for your organisation, our team builds agentic automations and offers OpenClaw managed services to help you get from proof of concept to production.

For the full documentation on the agent command and related tools, see the OpenClaw agent CLI docs.