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Stop babysitting your AI. Build a self-checking Kanban workflow instead.

Julian Goldie Agency20 July 2026Watch on YouTube

Part of series

Ep. 9 · Zelfcorrigerende AI-agenten

Laat zien hoe je AI-agenten bouwt die zichzelf automatisch controleren en corrigeren via feedback-loops, zonder handmatige tussenkomst.

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Description

Want to make money and save time with AI? Join here: https://www.skool.com/ai-profit-lab-7462/about Free SEO Strategy Session 👉 https://go.juliangoldie.com/strategy-session?utm=AIPB Get a FREE AI Course + Community + 1,000 AI Agents 👉 https://www.skool.com/ai-seo-with-julian-goldie-1553/about AI struggles to judge its own work, which leads to endless re-prompting and wasted tokens. A multi-agent Kanban system solves this by separating the creator from the critic. By assigning specialized agents to distinct roles—planner, builder, and judge—the workflow handles quality control automatically. The judge role reviews output against a definition of done, providing detailed feedback or a pass/fail verdict, ensuring only high-quality content ships without constant human intervention. #AIAgents #WorkflowAutomation #Kanban #ProductivityHacks #AIStrategy

What you'll learn

  • Build a multi-agent Kanban system with specialized roles (planner, builder, judge) to automate AI workflows without constant oversight
  • A separate 'judge' agent evaluates output against a definition of done and provides detailed feedback or a pass/fail verdict
  • Separating creator and critic in AI systems prevents endless re-prompting and reduces wasted tokens

Frequently asked questions

How does a multi-agent Kanban system solve the problem of AI struggling to judge its own work?
By deploying specialized agents in distinct roles, including a 'judge' agent that evaluates output against defined criteria, the system automates quality control without requiring constant human intervention.
What are the three main roles in a multi-agent Kanban workflow?
The three roles are: planner (plans the work), builder (executes it), and judge (evaluates the output against a definition of done).
What advantage does separating creator and critic in AI systems provide?
It prevents endless re-prompting and token waste by having the 'judge' agent provide objective feedback or a definitive pass/fail verdict, rather than the same agent constantly adjusting itself.

Topics

ai agentsworkflow automationmulti-agent systemsquality control