LLM AI Agent Evaluations and Observability with Galileo AI — 95% Off Coupon

Build Robust AI Agents | Monitor Production AI Agents | Build Custom Evals | Master Galileo AI | For Engineers

⭐ 4.9 out of 5 Rating (156 students) Created by Henry Habib, The Intelligent Worker Updated: April 2, 2026 🌐 English

Key Takeaways

A summarized snapshot of the essential course data, author credentials, and live coupon verification statistics from our manual technical audit.

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Course Title: LLM AI Agent Evaluations and Observability with Galileo AI

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Provider: Udemy (Listed via CoursesWyn)

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Instructor: Henry Habib, The Intelligent Worker

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Coupon Verified On: April 2, 2026

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Difficulty Level: All Levels

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Category: Development

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Subcategory: Software Development Tools

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Duration: 7h 30m of on-demand video

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Language: English

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Access: Lifetime access to all course lectures and updates

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Certificate: Official certificate of completion issued by Udemy upon finishing all course requirements

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Top Learning Outcomes: Design an LLM observability plan: what to log, how to structure traces, and how to make failures diagnosable · Build evaluation datasets with realistic inputs, expected behavior, metadata, and slices for edge cases and regressions · Run repeatable Galileo AI experiments to compare models, prompts, and agent versions on consistent test sets

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Prerequisites: Basic Python knowledge · Basic AI Agent building knowledge · Can work with Jupyter Notebooks · No prior observability experience needed

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Price: $9.99 with coupon / Regular Udemy price: $199.99. Applying this coupon saves you $190.00 (95% OFF).

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Coupon: Click REDEEM COUPON below to apply discount

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What You'll Learn

The following technical skills represent the core curriculum targets for learners enrolling in this verified program today.

Design an LLM observability plan: what to log, how to structure traces, and how to make failures diagnosable

Build evaluation datasets with realistic inputs, expected behavior, metadata, and slices for edge cases and regressions

Run repeatable Galileo AI experiments to compare models, prompts, and agent versions on consistent test sets

Implement custom eval metrics for generation quality, groundedness, safety, and tool correctness (beyond accuracy)

Apply LLM-as-judge scoring with rubrics, constraints, and spot checks to reduce evaluator bias and drift

Debug agent failures using traces to pinpoint breakdowns in retrieval, planning, tool use, or response synthesis

Set up production monitoring in Galileo with signals, dashboards, and alerts for regressions and silent failures

Use eval results to prioritize fixes, validate improvements, and prevent quality or safety regressions over time

Choose observability and eval methods for single-call LLM apps vs. multi-step agents, and explain tradeoffs

Instrument LLM apps and agents in Galileo to capture traces, spans, prompts, tool calls, and metadata for debugging

Design an LLM observability plan: what to log, how to structure traces, and how to make failures diagnosable

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Requirements

Please review the following prerequisites to ensure you have the necessary tools and foundational knowledge for this training.

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Basic Python knowledge

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Basic AI Agent building knowledge

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Can work with Jupyter Notebooks

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No prior observability experience needed

About This Course

Comprehensive curriculum analysis and educational value proposition from the official provider library hubs.

Important note: Please click the video for more information. This course is hands-on and practical, designed for developers, AI engineers, founders, and teams building real LLM systems and AI agents. It’s also ideal for anyone interested in LLM observability and AI evaluations and who wants to apply these skills to future agentic apps. You should have some knowledge in AI agents and how they are built.

Note this is the complete guide to AI Observability and Evaluations. We go both into theory and practice, using Galileo AI as the AI Agent / LLM monitoring platform. Learners also get access to all resources and the GitHub code / notebooks used in the course.

Why does LLM Observability and Evaluations Matter?

LLMs are powerful, but they are unpredictable. They hallucinate, they fail silently, they behave differently across prompts and versions. There is a big difference between building an AI agentic / LLM system and actually "productionalizing" it. What if the LLM starts producing offensive content? What if tools embedded within agents fail silently? How do you measure model quality degradation?

Traditional monitoring and building methods don't work. You need to run experiments, build custom evaluations, and set up alerts that assess subjective measures. Dashboards built to track classification accuracy are not designed for open-ended text generation. Log pipelines created for predictable APIs cannot capture reasoning steps, tool usage, or why an agent failed.

As a result, most teams fall back on manual spot checks, gut feel, and endless prompt tweaking. That approach might work in the beginning, but it does not scale.

What we need instead is a systematic way to measure, monitor, evaluate, and continuously improve LLM and agent systems. That is where observability and structured evaluation come in.

What is this course?

This course will make you more confident when you build and deploy AI agents or other LLM-based systems. It will teach you the tools and tricks needed for building robust AI agents with structured personalized evaluations and experiments, and how to monitor your agents in production with observability and logging. We first start with the basics, the theory around what makes AI agents / LLM systems particularly difficult to build and track. Then, we get into the practical where we build our own evaluations and instrument our own apps with Galileo AI.

What is Galileo AI?

Galileo is a platform designed specifically for evaluating and monitoring LLM and agent systems. It's specifically designed for AI agents / LLM-based systems, and includes the following features:

Observability: Log LLM interactions, track spans and metadata, visualize agent flows, monitor safety and compliance signals
Evaluations: Design experiments, create evaluation datasets, define and register metrics, use LLMs-as-judges, version and compare results

In short, it gives you a structured way to understand how your AI systems behave and helps you build them. In this course, we do a masterclass in Galileo AI and how to use it to monitor and evaluate your AI app.

Course Overview:

Introduction - We start by explaining why LLM evaluations and observability matter, covering the risks of deploying generative AI without structured monitoring, setting expectations, and reviewing the course roadmap.
Theory: LLM/Agent Observability - This section introduces traditional monitoring concepts, explains why they fall short for generative systems, and outlines the key components of LLM observability.
Theory: LLM / Agent Evaluations - You’ll explore evaluation theory, understand why evaluations are critical for production AI, learn the main evaluation approaches, and see the common challenges teams face with LLMs.
Theory: Observability and Evaluations for LLMs vs Traditional ML - We contrast generative AI with classical machine learning, highlighting the unique risks, costs, and iteration loops.
Theory: Tools and Approaches for LLM Observability and Evaluations - This section surveys the landscape of observability and evaluation tools available for LLM systems and explains why dedicated platforms are necessary.
Practice: Galileo Platform Deep-Dive Overview and Setup - This section walks you through Galileo’s architecture, integrations, pricing, account creation, repository cloning, and local development setup to prepare you for instrumentation.
Practice: Logging LLM Interactions with Galileo - You’ll learn practical logging with Galileo, including terminology, manual and SDK-based methods, simulating LLM applications, inspecting agent graphs, detecting errors, and setting up alerts and signals.
Practice: Evaluating LLM Performance with Galileo - We shift from observation to evaluation, showing how to design experiments, manage datasets and metadata, implement evaluation code, define metrics, and perform agent-specific and LLM-as-judge assessments.
Conclusion: Earn your certificate

Meet Your Instructor

Academic background and professional track record of the subject matter expert responsible for this curriculum.

Henry Habib, The Intelligent Worker

Verified Architect

A global leader with specialized excellence in Development. Instructors are vetted for curriculum quality, responsiveness, and consistent student success across the Udemy platform.

4.8 / 5.0

Instructor Rating

94% +

Success Rate

Course Comparison

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Feature Benchmarks	This Verified Offer	Global Standard
Cost Verification	FREE (100% Validated)	Fixed Subscription Fee
Enrollment Type	Professional Lifetime Access	Limited Time Ownership
Certification Award	Included with Access Code	Required Add-on Fee

Expert Review

Andrew Derek

Lead Course Analyst, CoursesWyn

"After auditing the curriculum depth and verifying the live access protocol, LLM AI Agent Evaluations and Observability with Galileo AI stands as an essential career asset. For a verified cost of $0, the return-on-learning ratio far exceeds commercial alternatives."

✅ Strategic Advantages

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Official Certificate: Credential generated at no cost.
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Mobile Friendly: Full access via smart TV & mobile.
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Expert Pacing: Modular design for professional schedules.

❌ Considerations

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Technical Depth: Requires focused 10+ hours study.
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Tool Prep: Certain labs require proprietary software setups.

Verification Outcome: Exceptional Academic Value

Course Rating

Collective learner data and performance analytics based on verified alumni feedback loops and technical graduation audits.

4.9

★★★★★

Verified Excellence

5 Stars

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Frequently Asked Questions

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Andrew Derek

Expert Reviewer

Andrew Derek is a lead editor and course analyst at CoursesWyn with over 8 years of experience in online education and digital marketing. He meticulously audits every Udemy coupon and course syllabus to ensure students get the highest quality learning materials at the best possible price.

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