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Building AI Workflow Automation with OpenClaw: From Idea to Production
How we use OpenClaw to automate repetitive work—meetings to tickets, email triage, status updates—with human approval loops and audit trails.
How we use OpenClaw to automate repetitive work—meetings to tickets, email triage, status updates—with human approval loops and audit trails.
AI agents are finally useful. Not because the models got smarter (though they did), but because the infrastructure got better.
At OpenClaw Labs, we use OpenClaw—an open-source agent framework—to automate the boring parts of work. Things like:
These aren't "AI demos." They're production workflows that save 5-10 hours per person per week.
Here's how we build them.
Not every task should be automated. We look for workflows that are:
Examples that fit:
Examples that don't:
We use OpenClaw because:
It's not the only option (you could use n8n, Zapier, Make, or custom code), but it's the best we've found for agent-driven workflows where the AI makes decisions, not just moves data.
The Problem: After every sprint planning or customer call, someone has to manually create Jira tickets. It takes 20-30 minutes and often gets delayed.
The Solution: An OpenClaw agent that:
Human-in-the-loop: A team lead reviews the proposed tickets, edits if needed, and clicks "Create." The agent logs everything.
Impact: 25 minutes saved per meeting. Over a month, that's ~7 hours for a team that has 15 meetings.
# Simplified OpenClaw config
agent:
name: meeting-to-jira
triggers:
- event: google_drive.file_created
filter: "mimeType contains 'audio' or name contains 'Meeting Notes'"
actions:
- transcribe:
provider: deepgram
- extract:
prompt: |
Extract action items from this transcript.
For each item, return: title, description, owner, priority.
- create_draft:
target: jira
fields:
project: ENG
issue_type: Task
- post_to_slack:
channel: #sprint-planning
message: "New tickets drafted from today's meeting. Review here: {link}"
buttons:
- label: "Approve & Create"
action: jira.create_issues
(This is pseudocode—actual OpenClaw configs are more detailed, but this shows the structure.)
The Problem: Sales team gets inbound leads via HubSpot. Someone has to research the company, check LinkedIn, draft a personalized first email, and update the CRM. Takes 15-20 mins per lead.
The Solution: An agent that:
Human-in-the-loop: Sales rep reviews, edits, and sends (or discards).
Impact: 15 minutes → 2 minutes. Lead response time drops from 4 hours to 30 minutes.
The Problem: Every Friday, managers manually pull data from GitHub (commits, PRs), Notion (docs, project updates), and Slack (key decisions). Takes 30-45 minutes.
The Solution: An agent that:
Human-in-the-loop: Manager reviews before it's shared widely. Can add context or edit tone.
Impact: 45 minutes → 5 minutes. More consistent updates.
From 30+ deployments, here's what we've learned:
Never let an agent send an email, create a ticket, or post publicly without human review. Draft → Review → Execute.
Log every action: what the agent saw, what it decided, what it did. When something breaks, you need to reconstruct why.
What happens if the API is down? If the prompt returns garbage? If the human doesn't respond? Build fallbacks.
Don't try to automate everything at once. Pick one workflow, make it bulletproof, then add more.
Track time saved, adoption rate, and error rate. If people stop using it, figure out why.
Our typical engagement:
You get:
If your team spends hours on repetitive ops work—reporting, triage, follow-ups—we can help you ship agents that actually save time.
Book a 30-min discovery call →
More on OpenClaw:
OpenClaw GitHub →
OpenClaw Docs →
We run hands-on workshops and ship workflow automations for engineering and ops teams.
Book a 30-min discovery call →