Building LLM Agents for Enterprise Workflows

Introduction

Constrained finite-state machines beat unpredictable reasoning loops in regulated enterprise workflows.

An AI agent is useful only when its allowed actions can be constrained and evaluated. I use explicit state and limited tools for agentic systems; the BMRC project discussed below is a separate documented n8n, OpenAI, Supabase and Gmail workflow, not evidence that it used LangGraph or RAG.

State machines beat free-form loops

The biggest mistake in agent design is an open-ended 'think until done' loop. In production I model agents as explicit graphs with LangGraph: each node has a clear responsibility, and transitions are constrained. This makes the agent auditable — critical for healthcare and enterprise compliance.

Grounding agents with RAG

Retrieval is relevant when an application needs approved, changing source material, but it is not required for every agent. The retrieval layer and the action workflow should be evaluated separately.

The rule I follow

Give the agent the smallest possible action space, the richest possible context, and full observability. Autonomy without auditability is a liability, not a feature.