Tata Consultancy Services

Wipro

Serco Group

Dheeraj Saxena

Ex-IBM, TCS, Wipro Consultant. | 25+ Years Scaling Data, AI & MarTech Solutions

Why Enterprise AI Agents Fail Beyond The Demo.

Ex-IBM, TCS, Wipro. Executive Architect | 25+ Years Scaling Customer Data, AI & MarTech Solutions

Understand how principles replace developer-dependent "heroics" with scalable, repeatable enterprise standards.

Prevent AI Agent Sprawl at Scale

Recognize the architectural anti-patterns that cause AI initiatives to collapse beyond the demo.

Identify Enterprise AI Failure Patterns

Review 15 real-world principles like separate evaluation, deterministic routing, state management and more.

15 Examples Of Principles in Agentic AI

This is a project-based course. In 6 weeks, you will learn a repeatable framework and build your multi-agent AI application ready to launch.

Cohort 4

Cognizant BR Turma 1

Pearson SME, O'Reilly teacher, ex-Cisco

Cisco

Claro Brasil

Pearson

O'Reilly

Carmelo Iaria

Agentic Architect, Pearson SME, Startup Founder, 30 years of global experience

Cohort 3

Cohort 5

What alumni say<ul><li>Matt (Founder, SAVANT-AI): "Five stars for course logic, course structure, and course value."</li><li>Alexa (Lead UX Designer, Caseware): "I can already see how to bring it into my work."</li><li>Satyan (Group PM, AT&amp;T): "The concepts taught here make the difference between building a product that works and one that doesn't."</li></ul>Why this course mattersIn 2026, engineering value is shifting from task execution to orchestration and systems thinking. Anthropic’s research shows that being a software engineer increasingly means orchestrating agents that write code, evaluating their output, and ensuring the system solves the right problems—not just writing more code yourself. The gap between early adopters and everyone else is widening fast.You will leave with a production-ready capstone architecture, reusable templates, and a delivery playbook you can apply immediately.NOTE: <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/carmelo-iaria/agentic-architect?promoCode=EARLYBIRD">Promo Code "EARLYBIRD" 20% discount expires on May 17th</a>NOTE: Those enrolled to this course can join my <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/carmelo-iaria/agentic-architect-workshop">Workshop </a>for free

Software development is moving from writing code to orchestrating agents.

Define a solid foundation with clear research and project scope

Architect your complete multiagent solution — from roles to workflows

Design and build a production-ready Agentic AI Application

Define and configure your multiagent crew — from backend logic to integrations

Add enterprise-level quality: integrate human feedback, observability, and QA

Deploy, validate, and present your final Capstone solution with confidence

Own delivery outcomes in tech/product/design/consulting. Read-level Python is helpful; pair with a technical teammate if needed.

Experience & Background

Comfort with Git/GitHub, API basics, and AWS (or similar). If rusty, budget extra setup time in Weeks 1-2.

APIs & Cloud Platforms

Agentic IDE (Cursor, Claude Code, GitHub Copilot), OpenAI API credits (~$20+), GitHub account, AWS access (recommended)

Required Tools & Access

Technical Builders (software, AI/ML, data, platform) moving from demos to reliable delivery..

Product and Business Owners (PMs, founders, consultants) responsible for outcomes, priorities, and execution quality.

UX and Experience Leaders (UX, service design, research) shaping human-agent workflows, trust, and adoption.

How to think through AI Agent problems end-to-end: scoping, debugging, and knowing when to pivot.

AI Engineer with 10+ years of experience

Hugging Face

DataRobot

Contextual AI

Snowflake

Snorkel AI

Rajiv Shah

Agentic AI Engineer at OpenHands

Winter/Spring 2026

Summer 2026

AI Problem Framing is to AI teams what System Design is to software engineers and Product Sense is to PMs. Whether you're building, evaluating, or leading AI work, this is the foundational thinking skill that separates results from rework.--🚨 Summer cohort: June 1 – June 26 🚨Use Code: Earlybird (20% discount if you sign up by May 15th)---What you get:🔄 GOATS, a 5-step framework you'll use to think through every AI project🛠️ Bring your own AI Agent project and apply the frameworks each week (or use a provided case study)🧑‍🏫 2 Live sessions per week plus office hours📊 Access to 200+ case studies, the largest collection of AI reframing examples📋 250+ page PDF manual + video content that covers Agentic AI, Gen AI, and ML use cases🎥 Lifetime access to all recordings---This course is for you if:• Your AI works in demos but fails in production• You've spent months on a problem only to realize you solved the wrong problem• You lead AI work but came from engineering, product, or PMYou'll learn from 200+ AI failures in 4 weeks. You gain years of experience and recognize the scars.Student or between jobs? Message me. Financial assistance is available

Evals aren't enough, Learn the power of reframing AI problems

Frame AI problems right the first time

Diagnose what's actually broken

Own the problem, not just the solution

Steer your team past expensive mistakes

We focus on framing and strategy, not explaining what models or training means. Basic vocabulary lets us go deeper faster.

You understand what AI/ML is at a conceptual level.

Real experience gives you context. The frameworks click when you can map them to projects you've actually built or managed.

You've worked on or led AI projects in some capacity.

This is about decision-making, not implementation. You'll learn what to build and why, not how to code it.

No coding during the course. Its about frameworks for thinking, not tutorials.

Engineers who can build AI but want to master outcome engineering: knowing what agent to build, not just how

Building agents, RAG pipelines, or ML models? Let's get your demos into production! Learn the thinking that makes the difference.

Managers who need to evaluate AI project proposals, set realistic milestones, and know when to escalate

Learn how to define task autonomy, decision thresholds, guardrails, and human override in agent-enabled systems.

BA-Cube Founder | LinkedIn Instructor

Angela Wick

25+ years practicing, shaping, and teaching analysis, product, and requirements.

Agentic AI Skills for Business Analysts

Agentic AI changes the game because AI can autonomously act. It can initiate actions, make decisions, and operate across workflows with increasing autonomy. Risk escalates, and so does opportunity! Without deliberate, disciplined, and structured analysis, agents act on incomplete logic, unclear boundaries, and undefined escalation paths. That is where exposure multiplies. This course matters because Business Analysts are uniquely positioned to define autonomy limits, decision thresholds, guardrails, and monitoring structures before agents scale. It equips you to design control into autonomy, so AI-driven systems remain accountable, coherent, and aligned with business intent rather than becoming fragile at scale.AI is moving beyond assistance. Agents can:<ul><li>Initiate actions on their own</li><li>Make decisions</li><li>Trigger workflows</li><li>Operate continuously</li></ul>This changes the role of Business Analysis.When agents act without clearly defined:<ul><li>Goals</li><li>Boundaries</li><li>Escalation rules</li><li>Monitoring thresholds</li></ul>Organizations lose control.This course teaches how to design and govern agent behavior responsibly.

Prioritize, Define, Design, and Govern AI Agents and Agentic Systems

Distinguish Between Automation, Copilots, and True Agents

Decompose Tasks for Autonomous or Semi-autonomous Execution

Define Agent Goals, Success Criteria, and Measurements

Define/Design Multi-Agent Orchestration

Design Human Override and Intervention Paths

Participate in Ongoing Evaluation of Agent Performance

Senior and Principal Business Analysts/BA Leaders

Consultants advising on AI transformation

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.