Samsung

NOAA

UCLA

NASA

Direct Supply

Vyoma Gajjar

AI Developer, Lecturer, Advisor, Scout

What Your AI Agents Actually Cost:The Hidden Economics of AI

MIT Sloan Management Review

AI Architect | Lecturer | Advisor | Scout

Map how context regrowth, retrieval loops, and retries quadratically inflate bills.

Deconstruct multi-turn spend

Use unit-test eval harnesses to find the exact threshold where saving money breaks agents.

Map cost-vs-quality curves

Deploy simulation and edge-routing frameworks to lock down API exposure before going live.

Bound pre-production risk

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

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.

You stop arguing about whether the agent is good, and start showing the number, the rubric behind it, and the decision it justifies.

Author, AI Agent Evaluation (Springer)

Abdullah Mansoor

Co-author of AI Agent Evaluation (Springer 2026), written for non-coders.

Cohort 1

You shipped an AI agent. It demos beautifully. Then a board member, a customer, or your own gut asks the question you have been avoiding: how do you know it works? Most teams answer with more testing, more prompt tuning, and a dashboard nobody trusts. The gap does not close, because the missing piece is not effort. It is a system: a written standard for what good means, an instrument that measures against it, and a loop that runs on a schedule. This is the companion course to AI Agent Evaluation (Mansoor and Bokhari, Springer 2026), cut for the person who makes the release decision rather than writes the code. Six modules take you from naming the failure you are afraid of, to writing a contract your agent has to honor, to scoring it on four dimensions, to running a loop that improves it every week. Every tool, framework and metric is introduced at concept level. After a module you can hold your own with your engineers using these terms. No lesson asks you to open a terminal. You leave with seven artifacts built on your own agent: a contract, a rubric with anchors, a benchmark set, a failure taxonomy, a release gate, a cost and latency budget, and a seven-step playbook.

A demo proves it worked once. Production asks you to prove it works every time.

Name the failure pattern your agent is most likely to hit

Write a one-page agent contract

Score your agent on the four dimensions that decide readiness

Build a rubric two independent raters would score the same way

Run the Biruni Loop so quality improves on a schedule

Put cost and latency on the same row as quality

Every exercise is built on your own agent, so you leave with a contract and a rubric for something real rather than a worked example.

An AI agent in production, or one you are about to ship

Everything is taught at concept level. You will hold your own with your engineers using these terms without opening a terminal.

No coding required, and no prior testing or evaluation background

The templates only pay off against a decision you actually have to make, so bring a release you are genuinely unsure about.

Willingness to bring one real agent and one real release decision

Founders whose agent demos beautifully, then answers the same question two ways on a Tuesday. You need the rate, not the anecdote.

Product leads who own the AI feature but not the code. Engineers report a score. You cannot tell if the product improved or the test eased.

Technical founders who stopped coding. You read a system diagram fine. You want the vocabulary back at concept level, without a code tour.

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)

Google

Instagram

OpenAI

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

See your venture clearly, find what is holding it back, and choose the next move—so AI creates leverage instead of more work.

Systems Architect · Founder, Sysdom AI

THE WORLD BANK GROUP

Carnegie Mellon University

Han Kay

Systems Architect · Carnegie Mellon (MISM) · Triple Master's (MBA, MS) · 30+ yrs

AI gives founders two possible futures. It can multiply company capacity—or multiply the coordination work returning to the founder. If you're building an AI-native or agentic company, adding agents is the easy part. The harder problem is turning their output into leverage without becoming the integration layer for every handoff, decision, and next move. The missing layer is the system the agents operate inside. That is Systems Intelligence.This four-week working lab turns Systems Intelligence into founder practice. Across four focused 90-minute sessions, you will map your venture, identify the active constraint beneath the surrounding noise, commit one bounded next move, and complete a founder-directed operating cycle.Sysdom.ai is the included, supported working environment. It organizes venture context across the Product/Service, Customer, Cash, and Skills Engines, preserves evidence, and records the chosen move. You remain responsible for direction, consequential decisions, and approval boundaries.Leave with a clearer venture state, explicit decision boundaries, one completed cycle, and a repeatable way to increase agent capacity without increasing founder coordination.

AI amplifies everything. Including what’s broken.

See your venture as one system

Build an evidence-backed venture map

Find the constraint behind the noise

Choose one bounded next move

Set founder and agent decision boundaries

Start a guided operating cycle

Bring research, a prototype, operating signals, prior decisions, or a serious plan. No website, revenue, or technical setup required.

Bring one real venture—and some evidence

Founders building a venture who feel complexity outpacing their mental model. You have pieces but no unified operating system.

Founders who hit their first real bottleneck and realized working harder isn't solving the structural problem underneath.

Founders whose AI tools and agents produce more output while every important decision and handoff still routes back through them.

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

The AI-Powered Professional

Executives and senior leaders who set strategy, lead teams, and drive organizational outcomes.

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.

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

What Your AI Agents Actually Cost:The Hidden Economics of AI

What you'll learn

Why this topic matters

You'll learn from

Vyoma Gajjar