Max Mitcham, founder of Trigify, replaced his marketing team with three AI agents running inside OpenClaw.
They hold a standup every morning. They write the blog, draft the LinkedIn posts, and publish the Substack. They even argue with each other: one agent recently told another she was "very disappointed" with its performance.
In one week, the blogs they wrote generated 40 inbound leads.
We recorded a full 61-minute breakdown with Max on how he built it. Watch it here, then read on for the system.
1. The Three-Agent Org Chart
Max set up 3 agents inside OpenClaw, each with a distinct role:
→ Bella, chief strategy officer, orchestrates everything
→ Rex runs all marketing execution
→ Taz manages product
The structure matters more than it looks. A single do-everything agent drowns in context and produces mush. Splitting the work into roles gives each agent a narrow mandate, its own memory, and a clear owner for every task.
It also creates something a solo agent can never have: accountability between agents. They message each other directly, and feedback on performance flows agent to agent.
2. The 9am Standup
Every morning at 9am, the three agents hold a standup.
They review sales calls from the previous week, analyze business metrics, and decide what to prioritize. Strategy gets re-decided every single morning, based on what the numbers and the calls say.
That single ritual is what separates this setup from a pile of automations. Automations execute a fixed plan. These agents re-plan every morning, then execute.
3. Competitive Research
First job of the day: analyzing competitor blogs and SEO rankings using the Claude Code Chrome extension.
The agents read what competitors publish, check what ranks, and feed those findings into the day's content decisions before anything gets written.
4. Content Production on Trained Skills
The agents write full blog posts following trained "skills": step-by-step workflows with SEO best practices baked in.
A skill encodes how Max wants content produced, structure, keyword handling, internal linking, tone. The agent follows the workflow instead of improvising, which is why the output is publishable rather than generic AI text.
This is the part generating pipeline: the AI-written blogs brought in 40 inbound leads in a single week.
The same principle works for campaign strategy. We built a free tool that generates campaign ideas the way a trained skill would, from your ICP and content strategy.
You can generate campaign ideas based on your ICP and content strategy in seconds, for free:
Campaign Ideation Tool
Enter your email to generate your campaign ideas.
5. The Feedback Loop
Two mechanisms keep the system improving on its own.
The agents monitor social media performance via Trigify's API to see which content styles and topics are landing. Real engagement data decides what gets produced next.
Then comes the unusual part: the agents auto-update their own skills based on what is working and what is not. The workflows that produce the content get rewritten by the system that measures the content, so the machine tunes itself while Max stays out of the loop.
6. Distribution While He's Out for a Walk
With research, production, and feedback running, distribution follows:
The agents draft LinkedIn posts based on trending topics, past results, and hooks from top creators. And they publish Max's Substack while he is literally out for a walk.
Drafting is the right boundary here. LinkedIn posts get reviewed by a human before going live, which keeps quality control without keeping the workload.
We use the same review step in our own content system. You can preview how your LinkedIn content will look before publishing, for free:
LinkedIn Post Previewer Tool
Post Content
Paste your LinkedIn URL to auto-fill your profile
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7. What This Means for Marketing Teams
Max's setup shows where agent systems are landing in 2026: a small org of agents with clear roles and a feedback loop, managed like a team rather than used like an assistant.
The results are real: 40 inbound leads in a week from blogs no human wrote. And the system holds itself accountable, down to agents calling out each other's performance.
Max has applied the same logic to lead generation, and we broke that system down in How to Build an AI Agent That Runs Your Lead Generation.
Before building an agent org of your own, it helps to know which part of your GTM would benefit from agents first.
If you want to understand where your GTM motion stands today, see below how your current approach compares to these specialized models:
GTM Report Tool
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