Making my AI Agents Attack each others work

Описание: 🎬 Watch the full video:    • Advanced AI Coding Workflows: Agent Teams,...  

Discover a powerful technique for improving your AI-driven software development: setting up an AI "Red Team" to attack your own system. In this clip, I walk you through the exact process of creating an adversarial team of AI agents designed to find flaws and weaknesses in your code.

The core concept is to frame a primary AI agent as the leader of an external consulting firm. Your prompt should explicitly state that their job is NOT to agree with the existing work but to play devil's advocate. This team is tasked with hunting for bugs in logic, identifying over-engineered components that can be simplified, and uncovering other vulnerabilities before they become major issues.

Here's the workflow breakdown:
1. *Define the Role:* Instruct the agent that it's in charge of a team of external red team auditors.
2. *Set the Mission:* The goal is to attack the code, logic, and architecture to find weaknesses. The desired output is a list of isolated, actionable GitHub issues.
3. *Configure the Tool:* For this critical task, I use a model like Codex on a "very high" or "extra high" reasoning setting to dedicate maximum compute power to the analysis.
4. *Grant Autonomy:* Crucially, give the agents permission to run their own real-world tests (e.g., using MCP) to validate their findings. This allows them to move beyond static analysis and truly stress-test the system.

The result is a parallel workflow where your developer agents build the product while the red team simultaneously works to break it, leading to a much more robust and flawless final outcome.

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