LangSmith for AI Agents: Evaluate, Secure, and Ship

Name: LangSmith for AI Agents: Evaluate, Secure, and Ship
Rating: 4.2 (15 reviews)

Trace and evaluate your AI agents in LangSmith, add security guardrails, and deploy a production-ready application

Created byAnton Voroniuk• 1.250.000+ Students, Anton Voroniuk Support, George Paterakis

Last updated 9/2026

English

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What you'll learn

Set up tracing in LangSmith so you can see every step your agent takes and pinpoint exactly where a run went wrong
Build an evaluation pipeline with datasets and automated scoring to measure whether your agent is actually improving
Identify agent-specific security risks such as prompt injection and unsafe tool use, and put guardrails in place against them
Take an agent from a local script to a packaged, deployed application ready for real users
Read an agent trace step by step, find the failing node, and confirm the fix with a re-run
Apply a reusable security checklist before letting any agent act on real data or systems
Understand what separates a prototype from a production system — configuration, error handling, observability — and add each piece
Recognize the failure patterns behind most broken agents and design evaluations that catch them early

Course content

6 sections • 16 lectures • 2h 43m total length

Welcome to the Course: Evaluating and Shipping AI Agents with LangSmith0:31
A short introduction to the course goals, the final project, and the setup you'll need before your first traced run.

The Anatomy of an AI Agent and Why It Matters Now9:39
A precise definition of an AI agent and the reasons this pattern has become central to building with LLMs.
Under the Hood: The Loop That Drives Every Agent10:48
Trace the reason–act–observe cycle step by step so you can read an agent's trace and know where to look when it goes wrong.
Is an Agent the Right Solution? Knowing When to Say No5:46
A quick framework for spotting tasks that don't need an agent — often the cheapest fix for reliability problems.
The Failure Patterns You'll Be Learning to Detect12:41
The recurring ways agents break in production, framed as the exact behaviors your evaluations should catch.
Core Agent Vocabulary for the Rest of This Course19:50
A short glossary of the agent terms used throughout the evaluation, security, and production sections.

Why Agents Need Evaluation and Where LangSmith Fits In15:15
Understand why "it worked on my prompt" isn't evidence, and how LangSmith turns agent runs into measurable, comparable data.
Hands-On: Setting Up an Evaluation Pipeline for a LangGraph Agent9:36
Connect a LangGraph agent to LangSmith, build a dataset, and run evaluations that score its outputs automatically.
Tracing Runs and Debugging Failures Step by Step8:57
A practical reference for reading traces, finding the failing step, and confirming a fix with a re-run.

Closing the Gap Between a Prototype and a Production System17:58
See what separates a demo from a production agent — configuration, error handling, observability — and how to add each piece.
Hands-On: Building a Complete Production-Oriented Agent6:42
Apply evaluation, guardrails, and production practices together in one full build of a deployable agent.
Packaging Your Agent as a Real, Usable Application7:36
A guide to wrapping your agent in an interface or API so people can use it without running your code.
Going Live — Deploying Your Agent Beyond Localhost6:04
A step-by-step guide to moving your agent from a local environment to a live, running deployment.

Requirements

Working knowledge of Python (functions, classes, environment variables, basic packaging)
A working agent to evaluate — you should have built at least a simple agent in LangChain or LangGraph before
An API key for an LLM provider such as OpenAI or Anthropic (free tiers are enough)
A free LangSmith account (created in the course)
A computer with Python 3.10+ and a code editor installed

Description

Building an AI agent is only half the job. The other half is proving that it works, keeping it safe, and getting it into the hands of real users. This course covers that second half using LangSmith and production engineering practices, so your agent can move from a demo to a live application with confidence.

What you will learn

Trace every run of your agent in LangSmith and find the exact step where a failure happens
Build an evaluation pipeline with datasets and automated scoring to measure real improvement
Identify security risks specific to agents, such as prompt injection and unsafe tool use, and add guardrails against them
Understand what separates a prototype from a production system and add the missing pieces
Package your agent as a usable application and deploy it to a live environment

What is inside

The course opens with a short refresher on agent fundamentals, framed around the failure patterns you will learn to detect. From there it moves through three practical stages: measuring agent quality with LangSmith, locking the agent down with a threat model and a reusable security checklist, and shipping it as a complete production-oriented build. Each stage includes hands-on work and closes with a written reference you can return to at any time.

Who this course is for

This course is for developers who already have a working agent and need a reliable way to evaluate, secure, and deploy it. It suits AI engineers preparing to launch, backend engineers responsible for LLM features in production, and team leads who need a repeatable approval process before anything goes live. You should have built at least a simple agent with LangChain or LangGraph before starting.

Why take this course

It is short and focused on one outcome: an agent you can trust in production. There is no filler and no long theory marathon. You get tracing, evaluation, guardrails, and a deployment path, all applied to a real agent, in a few hours of focused work.

Enroll now and take your agent from working demo to production-ready application.

Who this course is for:

Developers who have a working agent and no reliable way to tell whether a change made it better or worse
AI engineers preparing to put an agent in front of real users and worried about what could go wrong
Backend engineers responsible for shipping and maintaining LLM-powered features in production
Team leads who need a repeatable process for evaluating and approving agents before deployment
Anyone whose agent works in a demo but breaks unpredictably and who wants to find out why

LangSmith for AI Agents: Evaluate, Secure, and Ship

What you'll learn

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Course content

Introduction1 lecture • 1min

Agent Fundamentals: A Refresher Before Evaluation5 lectures • 59min

Measuring Agent Quality with LangSmith3 lectures • 34min

Locking It Down: Security Risks and Guardrails2 lectures • 32min

Shipping It: From Working Prototype to Live Application4 lectures • 38min

Conclusion1 lecture • 1min

Requirements

Description

Who this course is for: