Uber

Directi

Morgan Stanley

Gaurav Sen

Founder @AIEngg, Software Engineer

Harness Engineering

Tanishq Singh

AI Engineer, M.Tech IIT Madras, previous work in healthcare & aviation sectors

Know what a harness is and its underlying components.

Know what is a harness

Identify the knowledge bases your agent needs to do its task.

Identify knowledge bases

Know how a harness makes an agent finish tasks effectively.

Know how a harness helps make effective agents

Master Advanced Techniques for Building and Optimizing Agentic RAG Systems and Multi-Agent Workflows — Designed for Builders

Founder | Ex-Google | Prof UCLA & UMN

Google

Walmart

University of Minnesota

Stanford Continuing Studies

UCLA

Hamza Farooq

Founder | Ex-Google |
Adjunct UCLA & UMN, SCU | Venture Partner

2026-03

2026-04

<h1>Master the full agent stack from first principles to production with RAG, voice, multi-agent systems, &amp; inference</h1>6 live sessions · 6 office hours · 1 demo day · 20+ hours<h3>What You’ll Build</h3>⚡ Agent Harness Design: Build an observable perceive–think–act loop in Python with structured tracing, leading to full agent systems with tools, reflection, and planning⚡ Agentic RAG Systems: Create advanced RAG pipelines with routing, multi-hop reasoning, Knowledge Graph, and semantic caching⚡ Real-Time Voice Agents: Engineer a streaming STT → LLM → TTS pipeline with turn-taking, interruptions, and sub-second latency⚡ Multi-Agent Orchestration: Design coordinated agent systems using MCP and A2A with hierarchical, debate, and orchestrator-worker workflows⚡ Guardrails &amp; Evaluation: Build reliable systems with safety guardrails and trajectory-based evaluation using LLM-as-judge and validated benchmark tasks⚡ Production Deployment: Ship multi-agent systems with Google ADK, MCP, A2A, Llama Guard, and GCP monitoring<h3>💻 Prerequisites: Python</h3>💥 Want to be a Claude Certified Architect?We are a member of the Claude Partner Network. Read the FAQs below for more information on how to get your certificate.

Build Production-Ready Agentic RAG, Voice & Multi-Agent Systems 

Build and Debug Production Agent Harness

Deploy and Optimize LLM Inference

Ship Agentic RAG with Graphs and Semantic Cache

Build Voice Agents That Feel Alive

Operate Multi-Agent Systems with Real Coordination

Evaluate, Guardrail, and Operate with Confidence

Full-stack and ML engineers going AI-native. Embed with customers to build and deploy AI products that actually get adopted.

Product managers who bridge customers and engineers. Turn customer problems into AI solutions and explain trade-offs to every stakeholder.

Software Engineer, looking to learn how to integrate AI into their products

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

Microsoft

Aishwarya Naresh Reganti

Applied AI @ OpenAI Codex | Ex-Google

OpenAI

Databricks

Samsung

Carnegie Mellon University

Kiriti Badam

Applied AI Lead | AI Advisor | Ex-Google

Cohort 1

The harness is the operating layer that manages an agent’s context, memory, tools, autonomy, and checks. Learn to engineer it, then build a harness and improve a browser agent.Self-paced: two core recordings, slides, readings, two assignments, and solutions. Study independently at your own pace. Individual feedback is not included.Current Offers:1. Live-cohort offer (when scheduled): 25% off with 25OFF2. 🎁 Full Stack AI Bundle — $2,950 early birdBecome a full-stack AI builder by year-end.Get this self-paced course with the bundle using <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aishwarya-kiriti/genai-system-design/11/join?promoCode=FSEARLY">this link</a>.<ul><li><a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aishwarya-kiriti/genai-system-design">Problem First AI</a> + the <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aishwarya-kiriti/openclaw-bootcamp">Agent-Native Operator</a> workshop</li><li>Advanced self-paced courses: <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aishwarya-kiriti/harness-engineering">Harness Engineering</a> and <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aishwarya-kiriti/evals-problem-first">Beyond Evals</a></li><li>Hands-on Portfolio Build Labs with selected partners to build your resume + $800–$1,000 in Partner Credits (Deepgram, Arize, NVIDIA, Cursor, Lyzr, Grain, Cartesia, Vapi, Mem0, Composio, Exa).</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>

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

October Cohort

November 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

Build AI solutions companies need. Gain practical skills, create your own product, and prepare for your next step in AI engineering.

The full program https://drive.google.com/file/d/1-hCMwFycaXnoJGqT04jh6pETEGFaCBiF/view?usp=sharing

September AI Engineer

October AI Engineer

CEO Hyperskill | Lecturer JetBrains, MIT

Nikolay Vyahhi

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

AI Engineer | Trained 150+ AI engineers

Nikalina Ogorodova

I help developers understand AI systems and turn that knowledge into working cod

CTO Hyperskill | Founder TheOna 

Ruslan Davletshin

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

AI Engineer & Researher

Ivan Rodin

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

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

June AI Engineer 

November AI Engineer

🛠️ Understand how AI applications work from input to output: what happens inside the model, how agents use tools and data, and how the whole system reaches real users.In this 10-week bootcamp, you'll develop practical AI engineering skills companies need to build, evaluate, and securely deploy AI solutions.You'll learn to answer questions like:<ul><li>Which model fits my task, budget, and privacy needs?</li><li>How do prompts, retrieval, memory, and tools work together?</li><li>How do I find failures and make AI behavior more predictable?</li><li>How can I deploy AI securely with company data?</li></ul>📚 View <a target="_blank" rel="noopener noreferrer nofollow" href="https://drive.google.com/file/d/1naKn7J9A643P7ygxS8RnC1wXSS66dg6R/view?usp=sharing">the syllabus here</a>💬 Learn through hands-on coding, live Q&amp;A, and instructor feedback. Build agents from first principles and bring your skills together in your own AI product.🌍 Our students come from Tesla, Microsoft, Adobe, Samsung, Salesforce, American Express and other companies to build practical tech skills through real projects.🚀 Build a portfolio and the engineering judgment to explain your decisions, apply AI at work, and prepare for your next career step.⏱️ Around 12 hours per week, including 1–2 hours of live sessions. Created by AI engineers at <a target="_blank" rel="noopener noreferrer nofollow" href="https://hyperskill.org">Hyperskill</a> Is this bootcamp right for you? <a target="_blank" rel="noopener noreferrer nofollow" href="https://savvycal.com/hyperskill-bootcamps/consultation-call-w-hyperskill">Book a call</a>

10-week Agentic  AI Engineering from experienced educators & practitioners

Choose the right LLM for your task

Build AI applications and agents from first principles

Connect AI to your own data

Evaluate and improve AI quality

Build secure AI systems and deploy them to real users

Build and present your own AI product

Be comfortable writing code and know basic Python. Preparation materials are available if you need a refresher.

Basic programming experience (Python preferred)

Plan for 10–12 hours weekly across projects, self-paced lessons, and live sessions.

Plan for 10–12 hours per week

Join live Q&A to discuss projects and get instructor guidance. See the cohort schedule for dates and times.

Join live discussions and Q&A

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

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

AI for GTM

Sales

Customer Success

GTM Engineering

Everyone

Founders

Operators

Data Scientists

Business Analysts

User Researchers

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Harness Engineering

What you'll learn

Why this topic matters

You'll learn from

Gaurav Sen

Tanishq Singh