Building Reliable Agents (LangChain Academy)
by LangChain
Take a customer-support agent from first run to production with tracing, datasets, LLM-as-judge and online evals.
Overview
Building Reliable Agents is a free, self-paced LangChain Academy course, listed on its course page as Agent Observability & Evaluation, that teaches the observe-evaluate-improve loop LangChain calls the Agent Development Lifecycle. Module 0 covers setup in either Python or TypeScript plus a glossary. Module 1, Observation, has lessons on observability, tracing with LangSmith to capture every LLM call and tool invocation, and analysing traces to debug an agent across versions. Module 2, Evaluation, covers why agents need automated evals, building datasets, running experiments, and three evaluator types in separate lessons: code-based evals, LLM-as-judge, and pairwise comparisons. Module 3, Moving Towards Production, covers moving from internal testing to real users, LangSmith's Insights Agent for analysing traces at scale, online evals on live traffic, and automations. The companion MIT-licensed repository, langchain-ai/lca-reliable-agents, mirrors every module in python/ and ts/ folders and builds one running example: Emma, a customer-support agent for a fictional company called OfficeFlow, with a SQLite inventory and a knowledge base. The agent evolves through versions v0 to v5: adding tracing, better tool instructions, a stock-information policy, no-chunking RAG and conciseness fixes. The course is built around LangSmith, so its tooling is vendor-specific, but the methods (datasets, evaluator types, online evals) carry over to other observability platforms.
At a Glance
- Topic
- Agentic
- Level
- Intermediate
- Format
- Course
- Cost
- Free
- Duration
- 29 lessons across 4 modules, self-paced (LangChain publishes no total-hours estimate)
- Provider
- LangChain
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Instrument an agent with LangSmith tracing to capture every LLM call, tool invocation and decision
- ✓Use traces to compare agent versions and locate the step where a run went wrong
- ✓Build evaluation datasets that make agent quality checks repeatable across changes
- ✓Run experiments that connect a target agent, a dataset and evaluators in LangSmith
- ✓Write code-based evaluators, LLM-as-judge evaluators and pairwise comparisons, and know when each one fits
- ✓Set up online evals and automations that monitor agent quality on real production traffic
- ✓Use the Insights Agent to surface recurring failure patterns across large volumes of traces
- ✓Iterate a RAG-backed support agent through successive versions driven by eval results
Highlights
- •Full Python and TypeScript parity, with each lesson's code mirrored in both languages in the companion repo
- •One realistic agent (the OfficeFlow support bot) improved over six versions, so each eval technique fixes a concrete failure
- •Covers all three evaluator families (code-based, LLM-as-judge, pairwise) plus online evals, not just offline testing
- •Rated 8.6/10 (Intermediate) by the course aggregator SkillsetCourse, which calls it a practical path from first run to production
- •Companion repo is MIT-licensed (85 commits, 24 forks when checked on 2026-10-01), so the code stays usable after the course
Who It’s For
Best For
- ✓Developers with a prototype agent who need a repeatable way to test and improve it
- ✓Teams already using LangChain, LangGraph or LangSmith that want a structured eval workflow
- ✓TypeScript developers looking for agent-evaluation material that is not Python-only
- ✓Engineers setting up production monitoring and online evals for an LLM application
Prerequisites
- •Comfortable programming in Python or TypeScript
- •Basic understanding of LLM agents, tool calling and RAG
- •A LangSmith account and an LLM provider API key to run the exercises
FAQ
What is Building Reliable Agents (LangChain Academy)?
A free LangChain Academy course for developers who already have a working agent and need to make it dependable. Using LangSmith in Python or TypeScript, you trace an agent, build evaluation datasets, run code-based, LLM-as-judge and pairwise evals, then set up online evals and automations so you can keep improving it after launch.
Is Building Reliable Agents (LangChain Academy) free?
Building Reliable Agents (LangChain Academy) is free to access.
What level is Building Reliable Agents (LangChain Academy) for?
Building Reliable Agents (LangChain Academy) is aimed at a intermediate audience. Recommended background: Comfortable programming in Python or TypeScript, Basic understanding of LLM agents, tool calling and RAG, A LangSmith account and an LLM provider API key to run the exercises.
How long does Building Reliable Agents (LangChain Academy) take?
Expect roughly 29 lessons across 4 modules, self-paced (LangChain publishes no total-hours estimate). Most learners work through it at their own pace.
What will I learn from Building Reliable Agents (LangChain Academy)?
You'll learn: Instrument an agent with LangSmith tracing to capture every LLM call, tool invocation and decision; Use traces to compare agent versions and locate the step where a run went wrong; Build evaluation datasets that make agent quality checks repeatable across changes; Run experiments that connect a target agent, a dataset and evaluators in LangSmith; Write code-based evaluators, LLM-as-judge evaluators and pairwise comparisons, and know when each one fits; Set up online evals and automations that monitor agent quality on real production traffic; Use the Insights Agent to surface recurring failure patterns across large volumes of traces; Iterate a RAG-backed support agent through successive versions driven by eval results.
Topics
Sources
This page was written from 3 sources, 2 on domains other than academy.langchain.com.