Debugging AI Agents at Scale: Credit Genie’s LangSmith…

Credit Genie shows how LangSmith’s tracing and Insights let them turn thousands of AI agent traces into targeted tests, cutting debugging time dramatically

Ever wonder how a fintech AI assistant keeps its cool when handling thousands of user queries?

⚡ TL;DR: Credit Genie uses LangSmith’s traceability and Insights to turn massive AI agent traces into targeted tests, debugging at scale without manual overload

The Simple Version

Think of LangSmith as a super‑powered microscope that lets you watch every step an AI agent takes

How It Actually Works

Trace Capture

LangSmith records each agent’s decision path as a trace

🎯 Think of it this way: It’s like having a flight recorder for a pilot who never lands

Insights Grouping

Insights slices traces by use case so you can focus on one problem at a time

🎯 Think of it this way: Sorting mail by zip code instead of reading every envelope

Technical Deep Dive

Credit Genie built AskGenie on LangGraph which orchestrates multiple agent steps

They attach custom evaluators that flag intent mismatches during trace analysis

Each trace is converted into a specific test question that targets a single behavior

Why This Matters

Debugging thousands of traces manually would take weeks

With LangSmith the team cuts that time to minutes

It also enables rapid iteration on financial reasoning without risking user errors

Key Takeaways

LangSmith provides full trace visibility for AI agents
Insights groups traces by use case for targeted debugging
Real traces become precise test questions
Custom evaluators catch intent mistakes early
Debugging time drops from weeks to minutes
Scales across thousands of production traces
Works seamlessly with LangGraph orchestration

Debugging AI Agents at Scale: Credit Genie’s LangSmith Secrets