Runjie Luo

Building Reliable AI Systems.

I design, evaluate, and investigate AI systems with a focus on agent reliability, observability, and human control.

Evidence·Evaluation·Observability·Human Control

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Featured Skills

Evidence

Can AI explain what it did? Building systems where every claim traces to a source.

RAG

Grounding

Citation

Evaluation

Can we measure AI quality? Designing evaluation workflows for agent reliability.

Model Evaluation

AgentBench

Benchmarks

Observability

Can we trace AI behavior? Investigating why agent systems lose their own history.

Traces

Events

Audit Logs

Human Control

Where should humans decide? Designing human-in-the-loop gates that actually work.

HITL

Approval

Workflow

Featured Project

AuditFlow

AI-powered Audit Intelligence & Reliability Research

An AI system exploring how autonomous agents can produce verifiable decisions — through document intelligence, RAG, evidence grounding, and workflow observability.

Python

FastAPI

LangGraph

PGVector

Docker

DeepSeek

PostgreSQL

Redis

View Details GitHub

Latest Investigations

Forensic reads into why AI systems drift from their design.

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Code Archaeology

Part 10

"I Went Looking for a Speculative Abstraction. I Found a Correct Architecture Boundary."

"A single-implementation strategy ABC turned out to be a correct dependency-inversion boundary — the series' first positive finding."

Code Archaeology

Architecture

Abstraction

Code Archaeology

Part 9

"The Handover Said 'Not Wired.' The Code Was Wired — to the Wrong Embedding."

"The handover doc said the Review Agent wasn't wired to grounding — it was wired from the start, but to the wrong embedding model."

Code Archaeology

Grounding

Evidence

Code Archaeology

Part 8

CRITICAL

"The Index Was 1024 Dimensions. The Queries Were 1536. The Search Never Worked."

"The knowledge base was indexed in 1024 dimensions; API queries ran in 1536 — two vector spaces in one system, and search could never work coherently."

Code Archaeology

Embedding

Vector Search