I Ran 157 Agent Plans Against a Real LLM. The Problem Wasn't Execution. It Was Planning.
dev.to·
An empirical field test analyzing 157 agent plans reveals that AI coding failures stem primarily from flawed planning rather than execution. When an LLM reviews its own plans, it consistently fails to identify flaws. Implementing a structured architecture—combining a dedicated planner, a distinct critic component, typed plan representations, and non-bypassable deterministic gates—dramatically improves safety and reliability. Crucially, deterministic gates do not process natural language input directly, making them immune to prompt injection attacks. Why it matters: As backend engineers transition toward staff-level systems design, building resilient agentic systems requires moving away from single-prompt loops toward rigorous multi-stage workflows. Understanding how to decouple planning from execution, enforce explicit abort paths, and audit diffs between plan iterations is essential for building trustworthy autonomous systems in production environments.