Nikhil Pentapalli

Senior AI Engineer | GenAI, RAG, Agents | Adobe | Ex-Startups | 9 Years in AI

Build a Production-Ready AI Code Review Agent

Adobe

O'Reilly Media

Cohere

Analytics Vidhya

Bold Penguin

Learn how to build an end-to-end AI agent that reviews GitHub pull requests

Build an AI Code Reviewer Agent
Description

Understand how to combine LLMs with tools, memory, skills, RAG, planning, and orchestration to create reliable agents

Design Production-Ready AI Agent Architecture

Get an overview of evaluation, prompt engineering, observability, and deployment considerations

Learn Production AI Engineering Best Practices

Build with the LLM APIs, RAG, LangGraph, MCP, A2A, evals, and agent memory, using Claude Code and Cursor, across 4 hands-on weeks. 

Cohort 8 [ongoing]

Cohort 9 [ongoing]

Cohort 11 [closed]

PhD in ML | Google AI Accelerator Alum

Google

Amazon Web Services

OpenAI

NVIDIA

Dr. Aki Wijesundara

AI Founder | Educator | Google AI Accelerator Alum

Exited AI Founder | CEO at Krybe

Boston Consulting Group (BCG)

McKinsey & Company

Manu Jayawardana

AI Advisor | Co-Founder & CEO at Krybe | Co-Founder of Snapdrum

Cohort 7 [Complete]

Cohort 10 [next]

Stop watching demos. Build production AI agents in 6 weeks.Most people have watched the demos. Few can build the system, then explain exactly why it works.In six weeks, you go deep on the fundamentals that actually matter:🧠 Prompting and context that hold up under pressure 🔍 RAG you can measure, fix, and trust 🔁 The agent loop built by hand, before any framework ✅ Evals serious teams run, with TRACE 💾 Agent memory that compounds across sessionsYou write real code, but fluency is not a prerequisite. Coding agents (Claude Code, Codex, and Cursor) help you skill up as you build.Past students include engineers and PMs from Google, Meta, Apple, Amazon, and Walmart. Graduates have gone on to land full-time AI Engineering roles at Google, TikTok, Capital One, and beyond.You leave with shipped systems, a portfolio you can show, and the understanding to defend every decision in them, including in an interview.Watch the free <a target="_blank" rel="noopener noreferrer nofollow" class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="https://theaiinternship.com/courses/think-like-an-ai-engineer/">Think Like an AI Engineer</a> recordings to get up to speed before week one. A limited number of need-based scholarships exist for applicants facing real financial hardship. <a target="_blank" rel="noopener noreferrer nofollow" class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="https://theaiinternship.com/scholarship">See if you qualify.</a> Check-out some of our student projects <a target="_blank" rel="noopener noreferrer nofollow" href="https://theaiinternship.com/student-projects/">here.</a>

PRICE GOES UP TO $1,999 IN 24 HOURS. OFFERS CAN'T BE COMBINED.

Engineer against a model, not just prompt it

Build RAG you can measure and actually trust

Build AI agents from the loop up

Run evals the serious teams run, with TRACE

Give your agent memory that compounds

Ship a portfolio that proves you can build

The one thing you really need. If you want to understand how AI systems work and build real ones, you are ready to start.

Curiosity and a drive to build

You do not need to be a strong coder. We build with coding agents, so you skill up as you build. Comfort with basic logic helps.

Some code, or willingness to pick it up

We use Claude Code and Cursor. They let you write and understand real code fast, even when it is new to you. Setup is covered in pre-course.

A coding agent to build with

The engineer moving into AI. Software, data, or ML. You want to build LLM systems and agents in production, not just talk about them.

The PM who wants to really understand AI. Done nodding along. Grasp RAG, agents, and evals deeply enough to lead and challenge a build.

The career mover. Targeting AI engineering or FDE roles. You need shipped systems and real understanding, not another certificate.

Build and reason about enterprise agents grounded in your team's knowledge base

Agentic Search Expert

Shopify.com

Reddit

Wikipedia

LexisNexis

Apple

Doug Turnbull

Led teams at Shopify, Reddit, Wikipedia

AI Builder/Educator (6 Million Students)

Instagram

Netflix

Yale

Hugo Bowne-Anderson

AI & data engineer, consultant, educator of 6+ million students (ex-Yale)

August 2026

Your company sits on endless reports, documents, and files. Agents leverage your company’s rich volumes of data to support customers and make business decisions.In this course agentic search expert Doug Turnbull partners with AI expert Hugo Bowne-Anderson to create agents that actually work.At the foundation of production agents sit an LLM, a reasoning loop, an agent harness, and a layer of context engineering + retrieval.Research consistently shows AI answer quality depends more on the underlying search than any other factor. We’ll go from the ground up - building an agent from scratch, giving you what you need to make them actually useful with accurate search + retrieval.You will leave with a working e-commerce agent, a retrieval pipeline, a starter eval set, and a deployment-ready architecture you can adapt to your own company data.Enroll before July 7 to get very early bird pricing at $750. Early bird pricing increases to $850 on July 7 and the full course price of $950 begins on August 1.

Real agents don’t just chat: they find, organize, and act.


Build agents from first principles

Ground agents in enterprise knowledge

Engineer reliable agent harnesses

Evaluate, automate, and deploy agents

Data scientists, machine learning engineers who are sick &amp; tired of seeing and building prototypes &amp; want to ship reliable LLM applications

Software engineers who want to learn how to build Generative AI systems and learn the LLM software development lifecycle.

Search technologists who connect enterprise knowledge with AI systems with RAG + Agentic Serach

You leave with deployable systems and documented architecture — not slide decks.

AI & DeepTech Leader & Entrepreneur

Hike

Yatra

Apparel Group

Oracle

Bytelearn

Dr Ankur Narang

Three decades building HPC and AI systems processing billions of transactions

Enterprise AI Agent Architect

Adda247

Mobileum

Kush Khurana

AI product and deep-tech builder, Visiting Professor at Ashoka University

AI Systems Architect & Leader

Stryker

Samsung

Dr Aveek Brahmachari

Stryker Senior Staff Engineer; IIT-ISM, USF PhD; ex-DRDO, Samsung

Founding Cohort

A 4-week intensive for engineers mastering autonomous cognitive workflows — from cost compression and self-correcting loops to revenue-scaling agents that turn AI from a cost center into a profit driver.<a target="_blank" rel="noopener noreferrer nofollow" href="https://deepcorexlabs.com/courses/production-ai-agents">Course Webpage</a>Build The Economic Engine of Agents: This goes beyond the technical deep-dive. We focus exclusively on production-grade impact—engineering the measurable business outcomes that deliver exponential ROI.20x ROI via Cost CompressionAchieve 20x return on investment by reducing operational overhead — replacing manual human-in-the-loop processes with self-correcting agentic loops.100x ROI via Revenue ScalingUnlock up to 100x return on investment by deploying agents that solve high-intent, real-time use cases — turning AI from a cost center into a primary profit driver.Achieve 5–10x lower AI infrastructure costsToken routing, caching, and cost modeling that saves six figures at scale.Systematic evaluation capabilityInternal eval infrastructure — catch regressions before users do.De-risked architecture decisionsBuild-vs-buy calls backed by production data, not vendor pitch decks.Reusable patterns across teamsOne cohort creates blueprints your entire engineering org can adopt.

Deploy Agentic systems that deliver 20x–100x return on investment.

Ship a functioning agent with structured tool use and state management.

Ship a retrieval pipeline that's grounded, verified, and citation-backed.

Ship cost controls and failure recovery into your agent.

Ship your capstone with full eval harness and deployment pipeline.

<h3>Engineering &amp; Product Leaders</h3>Responsible for shipping AI features. Need production patterns beyond prompt engineering and API wrappers.

<h3>Technical Founders &amp; CTOs</h3>Built the prototype yourself. Need it reliable enough for paying customers and investor due diligence.

<h3>Lead Developers</h3>You're the person who makes the AI system actually work — and keeps it working when inputs get messy.

Master the decisions that take AI agents from demo to production, and 
  become the engineer your team trusts with anything LLM.

Architect LLM and agent systems that survive production: cost, failure, evals, observability.

July 2026

Principal Engineer | AI & System Design

Ehsan Gazar

Principal Engineer, 500+ mentees, 16 years in production

October 2026

You can wire up an LLM and demo an agent that wows the room. Then it meets production, and everything you weren't taught shows up at once: costs spiral, latency balloons, the model does something confidently wrong in front of a user, and you have no evals to catch it and no traces to debug it. The demo was the easy part.If you're the engineer now expected to ship AI features, that gap is yours to own, and the field moves faster than anyone can teach it. So you improvise, lean on framework tutorials that stop at the happy path, and quietly hope it holds.This intensive teaches the part the tutorials skip: the architecture decisions that make LLM and agent systems survive real traffic, real cost ceilings, and real failure. Where to draw the boundary between code and model. How to bound cost and latency. How to design for non-determinism, evals, observability, and human oversight.You leave with production-grade artifacts you can use at work on Monday: an agent architecture doc, a cost and latency budget, an eval harness plan, and a failure-mode runbook. Not another demo. A system that holds.

Build LLM and agent systems that survive real traffic, cost, and failure.

Draw the right boundary between code and model

Bound cost and latency under real traffic

Design for non-determinism and failure

Choose the right agent architecture

Catch quality regressions with evals

Operate agent systems with confidence

A product or backend engineer suddenly asked to ship LLM features, who can build a demo but hasn't taken one to production before.

A senior or staff engineer responsible for AI systems in production, who wants real rigor around cost, failure, evals, and architecture.

A tech lead or architect evaluating agent patterns for their team, who needs to choose designs that scale and won't break under load.

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

Go-to-Market

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

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

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.

Using AI coding assistants and tools to write, review, and ship code faster.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.