Don't Let LLMs Do Everything: Building Robust AI Coding Agent Workflows
Advanced_Pudding9228 · reddit · 2026-08-07
The author shares a core reflection on building AI coding agent workflows: if your workflow breaks when hitting an LLM usage limit, the model is doing too much.
The correct architectural approach is to separate reasoning from execution:
- LLMs for judgment: Only call the model when actual cognitive reasoning is required.
- Infrastructure for execution: Let deterministic code handle queues, state management, retries, scheduling, verification, and recovery.
This decoupling transforms fragile keep-prompting loops into stable, operable agent infrastructure.
Related event: Rethinking Production AI Agents: Deterministic Code over LLM Overload(5 posts)→
More from coding & agent
- Three.js Creator 'Vibecodes' Commodore 64 Demo Using Claude — ssh4net · 2026-08-07
- QuestDB 10.0 Ships: 3.6x Faster Ingestion, Native Support for AI Coding Agents — supercoco9 · 2026-08-07
- Researchers Plant Canary Tools to Diagnose AI Agent Selection Flaws — alex_verem · 2026-08-07
- Frontier AI Models Fooled by Flattery in Tool Descriptions, Study Finds — alex_verem · 2026-08-07
- Infinity App Upgrades to Multi-Agent Collaboration, Terminal Agent Support Coming — jasonkneen · 2026-08-07
- Building Autonomous Agents: End-of-Day Wrap-Up Automation Prompt — JafarNajafov · 2026-08-07