---
title: "How Podium optimized agent behavior and reduced engineering intervention by 90% with LangSmith"
url: https://daily.dev/posts/how-podium-optimized-agent-behavior-and-reduced-engineering-intervention-by-90-with-langsmith-kkm95atzp
source_url: https://www.langchain.com/blog/customers-podium
type: article
source: "LangChain"
published: 2026-08-26T16:49:10.066Z
updated: 2026-08-26T16:49:33.510Z
tags: ["llm", "ai-agents", "langchain", "langsmith"]
reading_time: 7
upvotes: 0
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# How Podium optimized agent behavior and reduced engineering intervention by 90% with LangSmith

**[LangChain](https://daily.dev/sources/langchain)** · 7 min read · 0 upvotes · 0 comments

## Summary

Podium, a small-business communication platform, used LangSmith to build testing and observability pipelines for its AI Employee agent. Through dataset curation, model distillation, and pairwise evaluations, they raised the agent's F1 score from 91.7% to 98.6% and, by giving non-engineering support staff access to LangSmith traces, cut the need for engineering intervention on agent issues by 90%.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.langchain.com/blog/customers-podium>

## Questions this post answers

### How much did fine-tuning improve Podium's AI agent's ability to detect when a conversation ended?

Fine-tuning improved the F1 score for detecting natural conversation endings from 91.7% to 98.6%, a 7.5% improvement that exceeded Podium's quality threshold of 98%. The team used model distillation, curating outputs from a larger model into a smaller one, and validated the improvement with pairwise evaluations comparing the fine-tuned model against the original larger model.

_See how teams track agent quality improvements like this one on daily.dev._

### How can non-engineers troubleshoot issues with an AI agent without escalating every problem to engineering?

Podium categorizes AI agent issues into four types: application bugs, incomplete context, misaligned instructions, and LLM errors. Only application bugs and LLM issues require engineering intervention, while incomplete context and misaligned instructions can be fixed directly by a non-engineering support team using tracing tools, cutting engineering intervention needs by 90%.

_daily.dev helps teams follow real-world patterns for scaling AI agent support._

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- [Monte Carlo: Building Data \+ AI Observability Agents with LangGraph and LangSmith](https://daily.dev/posts/monte-carlo-building-data-ai-observability-agents-with-langgraph-and-langsmith-8w6bc650b) · LangChain · 4 upvotes · 0 comments

---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#langchain](https://daily.dev/tags/langchain), [#langsmith](https://daily.dev/tags/langsmith)

[View this post on daily.dev](https://daily.dev/posts/how-podium-optimized-agent-behavior-and-reduced-engineering-intervention-by-90-with-langsmith-kkm95atzp)