Building LLM Agents for Enterprise Workflows

An AI agent is only useful in the enterprise when it can be trusted to act. As an LLM Engineer in India, I build agents with LangGraph, RAG and GPT-4 or Claude that operate inside real business workflows for clients like BMRC Hospital.

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.

Agents hallucinate when they lack context. I ground every reasoning step in a retrieval-augmented generation (RAG) layer backed by a vector database, so answers cite real internal data instead of the model's imagination.

For the BMRC Hospital AI Feedback Agent, this meant multilingual patient feedback was classified, sentiment-scored and routed to the right team automatically — with a human always able to inspect why a decision was made.

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