Design pattern: LLMs handle ambiguity while deterministic rules enforce safe execution
goyalshaliniuk · x · 2026-09-16
A thread on a hybrid "LLM + rules" pattern for AI workflow automation: the LLM understands requests, handles ambiguity, and picks the next step, while deterministic rules control workflow, permissions, and safety.
Two concrete architectures:
- Workflow automation system: classify incoming tasks → route to the right team → require human approval for high-impact actions → block unauthorized tool calls
- Compliance assistant: LLM summarizes policies and explains regulations; the rules layer enforces mandatory requirements — checking approval conditions, restricting sensitive actions, applying access permissions, and escalating compliance risks
Core argument: AI provides flexibility and semantic understanding, while deterministic logic enforces permissions and execution constraints — the division of labor is what makes such systems both flexible and safe.
More from coding & agent
- Open-source Orca runs 5 Claude Code agents in parallel, hits 60k GitHub stars — alex_verem · 2026-09-16
- claude-reflect: open-source tool turns your corrections into permanent memory for Claude Code — tom_doerr · 2026-09-16
- Free Complete Guide to Obsidian Automation released, covering AI agents on a 20,000-note vault — dSebastien · 2026-09-16
- DaedalMap MCP Connector Serves Global Hurricane Tracks Dating to 1842 — modelcontextprotocol · 2026-09-16
- DoorDash MCP Server Brings Drive API Delivery Management to AI Agents — modelcontextprotocol · 2026-09-16
- Creator Runs Her Whole Business From a Phone: Agents on Every Tool, Obsidian as OS — evielync · 2026-09-16