An execution loop architecture for GPT-6 Astra agents
The author maps an Astra agent loop with separate PLAN, CODE, TEST and VERIFY layers, feeding failed test results back as new information for the next attempt.
GPT 6 Astra doesn’t need another prompt It needs an execution loop that knows when its own work is wrong Most people build AI agents that complete a task, return an answer and stop But what happens when the code fails, the output is incomplete or the agent confidently makes the wrong decision? That’s where the architecture gets interesting I mapped out an Astra execution loop with separate layers for planning, coding, testing and verification → PLAN breaks the objective into executable tasks → CODE turns those tasks into actual changes → TEST runs checks instead of trusting the generated output → VERIFY decides whether the result is acceptable or needs another pass The critical part is the feedback A failed test shouldn’t just produce another paragraph explaining what went wrong It should become new information the agent uses on its next attempt And every iteration should leave behind evidence of what changed, what passed and what still needs attention The real bottleneck in autonomous coding isn’t generating more code It’s knowing when the work is actually finished Save this architecture before building your next AI agent
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