Building a Production-Grade Multi-Agent LLM Framework

Enterprise clients don't just need AI that answers questions — they need AI that can reason across multiple steps, retrieve the right context, and produce outputs reliable enough to act on. This project was about closing that gap: taking LLM capabilities from experimentation to a framework that teams could actually build on.

Our clients were running early LLM pilots that hit the same ceiling: single-turn prompting, no structured retrieval, no guardrails around output quality. Results were inconsistent and hard to evaluate. The ask was to build something that could scale.

I architected and led development of a multi-agent LLM framework using LangGraph, designed to handle complex, multi-step reasoning tasks across different client use cases. Key components included retrieval pipelines — integrating vector search with prompt orchestration to ensure agents were grounding responses in relevant, current context — agent orchestration with a graph structure allowing specialised sub-agents to hand off tasks cleanly with state managed across steps, and an evaluation framework built to measure retrieval precision, hallucination rate, and response quality at scale.

Multi-agent systems fail in subtle ways. An individual agent might produce correct output, but the composition breaks — wrong handoff, wrong context window, wrong tool call. A significant part of the work was designing failure modes to be visible and recoverable, not silent.

The framework became the foundation for multiple client engagements. It reduced the time to deploy new LLM-powered features significantly, and gave teams a shared evaluation language for what "good" looked like.

Skills: LangGraph · LangChain · RAG · Vector Search · LLM Evaluation · Python · System Design