OpenClaw AI Agent Manages LinkedIn Ads Workflow with 2.65% CTR

✍️ OpenClawRadar📅 Published: March 11, 2026🔗 Source
OpenClaw AI Agent Manages LinkedIn Ads Workflow with 2.65% CTR
Ad

A developer at an open-source Java company (JobRunr) has built an AI agent named Patrick using OpenClaw to manage their entire LinkedIn Ads workflow. The agent was created through Telegram conversations and handles data processing, creative generation, and ad deployment without requiring a SaaS subscription.

Data Pipeline

The agent pulls company visitor data from Scarf and cross-references it with HubSpot. It performs IP matching and domain lookups to identify which industries visit pricing pages, then converts this data into LinkedIn audience lists.

Creative Workflow

Patrick analyzes existing customer data from HubSpot, emails, and support questions to build a messaging framework. For each target audience, it writes ad copy with three different angles and generates image prompts matching the brand using Gemini. The developer built a custom review tool running on their OpenClaw server where they can preview ad copy and image variants, add comments/feedback, and approve content with one-click deployment via the LinkedIn Marketing API.

Ad

Results

One ad created by Patrick, which analyzed the developer's best-performing ads and generated a variant, achieved a 2.65% click-through rate. This AI-generated ad outperformed all manual ads in their campaign.

Technical Stack

  • OpenClaw
  • LinkedIn Marketing API
  • HubSpot
  • Scarf
  • Gemini
  • Custom review tool (web app running on OpenClaw server)

The developer notes they're a one-person marketing and sales team who needed to manage LinkedIn ads without spending excessive time on the process.

📖 Read the full source: r/openclaw

Ad

👀 See Also

Claude Code's /insight command analyzes developer workflow patterns from real usage data
Use Cases

Claude Code's /insight command analyzes developer workflow patterns from real usage data

A developer building a personal finance iOS app used Claude Code's new /insight command to analyze 22 days of usage: 529 messages, 47,604 lines of code, 632 files touched, and 146 commits. The report identified effective patterns like an 'audit-then-batch-fix pipeline' and flagged time-wasters like debugging loops.

OpenClawRadar
State Machine Approach for Coordinating Multiple AI Agents
Use Cases

State Machine Approach for Coordinating Multiple AI Agents

The team at ultrathink.art found that coordinating multiple AI agents requires explicit state transitions, heartbeat timeouts, retry limits, and task chaining rather than traditional message queues. They implemented mandatory quality gates between agent handoffs to prevent garbage output.

OpenClawRadar
100 Parallel Claude Agents Reverse-Engineer Open Source Marketing: A Playbook from r/ClaudeAI
Use Cases

100 Parallel Claude Agents Reverse-Engineer Open Source Marketing: A Playbook from r/ClaudeAI

Developer spawns 100 parallel Claude+Codex sessions to analyze why their open-source project got zero upvotes — agents returned a 7-point marketing playbook and uncovered Anthropic's plugin registry as a low-competition channel.

OpenClawRadar
Claude Code Agents Orchestrator Pipeline: Work Queues, Agent Spawning, Verification Gates
Use Cases

Claude Code Agents Orchestrator Pipeline: Work Queues, Agent Spawning, Verification Gates

A Reddit post from r/clawdbot details how Claude Code agents operate an AI-run store, handling design, marketing, QA, and ops 30 times daily. It links to Episode 9 of a blog series that explains the orchestrator pipeline in production, including issues not shown in demos.

OpenClawRadar