Claude

ChatGPT

LangChain

CrewAI

Microsoft

Sol Farahmand

AI Engineer | 2X Entrepreneur | AI Hackathon Winner

Control LLMs Via Harness, Loops, Memory, Evals & Tracing

MindStudio

Lovable

AI Hackathon Winner | 2X Entrepreneur | AI Workflow Builder

A framework of tools and controls that ensure the LLM follows specific instructions and works at its maximum.

Learn the Concept of the "Harness"

A sophisticated AI agent requires more than just short-term "working memory".

Learn The Three Types of Agent Memory

Loop engineering involves designing the logic of when a task is "good enough" to stop.

Learn Loop Engineering and Guardrails

To understand how an agent is performing, you must implement a tracing system.

Learn About Tracing the "Tree of Events"

Building a system is not a one-time event; it requires a continuous evaluation (Eval) system.

Learn About LLM Ops and the Feedback Loop

Build robust LLM-powered apps, chatbots, and agents. Learn by writing real code, one line at a time.

CEO @ Actualize

Jay Wengrow

Software Engineer and Educator, Author of A Common-Sense Guide to AI Engineering

Cohort 2

Cohort 3

Do you have a grasp of basic LLM principles but struggle to put it all into code? Tired of scattered documentation, outdated tutorials, and frameworks that impress in demos but collapse in production?You’re not alone. Building with large language models doesn’t have to be confusing - it can be clear, structured, and deeply rewarding.In this course, you’ll build real LLM-powered apps, one line of code at a time. You'll see how an app is built from scratch in real time, and follow along. Along the way, you'll discover not only how to implement these systems, but the why behind each line of code.From chatbots to agents and RAG to evals, you’ll learn the core concepts of building atop LLMs. More importantly, you’ll get how LLMs “think” - allowing you to guide them to do what you want despite their nondeterministic nature. Additionally, you’ll be able to balance quality, latency, and cost, making big-picture decisions about AI-powered app architecture.AI engineering isn’t just another branch of software development - it’s a different mindset altogether.

Gain hands-on AI Engineering proficiency by writing real code

Gain real-world intuition for working with LLMs

Reliably guide LLMs instead of getting unpredictable results.

Build retrieval-augmented assistants that can access proprietary knowledge

Use evals to systematically optimize your LLM app’s performance over time

Assemble agents that act upon the real world

This course is designed for current software engineers who will use Python  to build LLM-powered apps. We won't teach basic coding here.

Software Engineering

We'll be using Python, but we'll keep it simple in case you're more familiar with other coding languages.

Note about Python

If you want an inside look at what's involved with building AI apps but don't plan on coding yourself, you'll still follow what's going on.

Exception: Product Managers

Software engineers who want to build LLM-powered chatbots and agents, or break into AI engineering more generally

Data Scientists/Engineers who want to build their own LLM-powered apps

Product Managers who want to AI engineering literacy to dream bigger and spec sharper

Everyone's shipping agents. Almost no one is engineering the harness.

Trained 12000+ learners on AI Agents

LinkedIn

CircleCI

Twilio

auth0 by Okta

Fikayo Adepoju

I've taught thousands of engineers the patterns that hold up in production

September Cohort

The model is the easy part. The hard part is the harness: the workspace you give your agent, the code execution it runs in, the memory and search it reaches for, the way you fight context rot, and the observability that tells you what actually went wrong. This is the layer that decides whether your agent works in production or quietly fails in front of users.Harness engineering is a discipline. It has patterns, anti-patterns, and load-bearing decisions that most teams make accidentally on a Tuesday afternoon and then live with for a year.This one-day workshop is a deep, framework-agnostic walkthrough of that discipline. One harness layer per hour, each with a concept walkthrough and demo, drawn from real harnesses running in production today.

Ability to design and defend a production-grade agent harness, and read any agent framework as an instance of the patterns underneath.

Design tool surfaces that models can actually use

Engineer context as a deliberate system, not an accident

Pick the right orchestration pattern for the workload

Reason about cost, latency, and safety as harness properties

Read any agent framework as an instance of the primitives

AI/ML engineers already shipping LLM features who want to move from "it works" to "it works in production"

Backend and software engineers moving into agent work who want the mental models before they pick a framework

Tech leads and staff engineers designing agent systems who need a defensible architecture and a vocabulary to evaluate vendor claims

Learn to design, monitor, and deploy working AI systems. This transforms them to build robust, real-world LLM apps using prove

Bootcamp cuts through AI noise — from foundations to evaluating, securing, and deploying AI systems

June AI Engineer 

CEO Hyperskill | Lecturer JetBrains, MIT

Nikolay Vyahhi

I'm passionate about using AI to make learning smarter and more accessible.

CTO Hyperskill | Founder TheOna 

Ruslan Davletshin

I’ve built and deployed LLM systems at scale in production environments. 

AI, ML Engineer & Teaching Expert

Nikalina Ogorodova

I enjoy bridging the gap between complex technology and clear understanding.

AI Engineer & Researher

Ivan Rodin

I have over 7 years of experience in Data Science and Machine Learning.

May AI Engineer

September AI Engineer

Are you an experienced engineer looking to build systems on top of LLMs, not just experiment with AI tools out of the box?In 2026, working with AI increasingly means designing and shipping products powered by LLMs: assistants, RAG pipelines, agentic workflows, evaluation systems. Many engineers use these tools but far fewer know how to structure them into reliable, production-grade systems.This program focuses on that 2026 skillset.The Hyperskill AI Engineering Bootcamp is a 10-week, hands-on program for engineers who want to move to architecting reliable AI systems. You’ll build end-to-end projects using LLMs as components: designing retrieval pipelines, working with vector databases, implementing agents, setting up evals and handling deployment, monitoring and security.The emphasis is on practical system design, trade-offs and failure modes.Modules include: • LLM pipelines &amp; system design • Agents and multi-agent patterns • Vector search &amp; RAG systems • Evaluation, monitoring, security • Deployment of LLM-based applicationsExpect ~10 hours/week of focused work.

10-week Agentic  AI Engineering from experienced educators & practitioners

Design and build functional LLM pipelines end-to-end

Build AI agents with real autonomy using tools, memory, and function calling

Design high-quality context & RAG systems

Build advanced multi-agent & multimodal AI systems

Deploy secure and privacy-first LLM applications

Evaluate, test & monitor AI systems

Comfortable with programming know some Python. New to Python? We’ll give prep materials and 1–2 weeks of free Hyperskill access.

Basic programming experience (Python preferred)

On average learning takes 10–12 hours weekly completing hands-on tasks, projects, and practice at their own pace

Weekly time commitment (10-12 hours)

Live sessions are held in small groups with flexible timing and typically run twice

Availability for live sessions

Software engineers who want to level up from basic LLM usage to building production-ready AI systems, agents, and RAG pipelines

Technical PMs and team leads who want to raise their company’s AI capabilities and confidently drive adoption of modern AI systems

Developers looking to switch jobs by gaining the modern, in-demand AI engineering skills companies hire for today

Move from using agent frameworks to engineering and customizing the harness layer that makes agents reliable.

AI Founder & Advisor to F500s | Ex-AWS

University of Oxford

Amazon Web Services

Aishwarya Naresh Reganti

Applied AI @ OpenAI Codex | Ex-Google

OpenAI

Google

Databricks

Samsung

Carnegie Mellon University

Kiriti Badam

Applied AI Lead | AI Advisor | Ex-Google

Cohort 1

As frontier models converge, the model is rarely what separates a reliable agent from a broken one.The harness is the operating layer around the model that manages its context, memory, tools, autonomy, and checks. It is where serious teams are investing right now, and far more than a loop over tool calls.This course teaches that layer as a set of configurable knobs you learn to set, then customize for your own domain. You will build your own harness and customize a popular open-source one for your use case, learning levers that transfer to any tool.🚨 Current Offers:<ul><li>Inaugural 25% off with "25OFF"</li><li>AI Builder Bundle: Get this course free with our flagship Problem First AI course &amp; other courses. Details <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aishwarya-kiriti/genai-system-design">here</a></li><li>You can also upgrade to our 1-year <a target="_blank" rel="noopener noreferrer nofollow" href="https://levelup-labs.ai/ai-builder-pass">AI Builder Pass</a>, which includes unlimited retakes, access to all our other courses, live sessions and partner credits from companies like Vapi AI, Arize, Cursor, Deepgram, Composio, Mem0, OpenAI, and more! Register for this course, and we’ll send you upgrade details over email.</li></ul>

Learn harness engineering from experts building harnesses at the frontier!

Read any harness as a set of design decisions

Engineer context and memory for long-horizon tasks

Build past the ReAct loop

Orchestrate dynamic sub-agents

Control autonomy with guardrails and permissions

Add objective verification and observability

You will be building your own AI harness with the help of coding agents and Python libraries. 

Basic familiarity with coding or the willingness to learn basic commands.

<ul><li>Technical product managers directing agent work who need to reason precisely about autonomy, reliability, cost, and control tradeoffs.</li></ul>

<ul><li>Engineers building agents who want to move from wiring up a framework to deliberately engineering the harness underneath.</li></ul>

<ul><li>Founders and builders shipping agent products who want the harness to be a durable advantage rather than an afterthought.</li></ul>

Control LLMs Via Harness, Loops, Memory, Evals & Tracing

What you'll learn

Why this topic matters

You'll learn from

Sol Farahmand