Mehran Mazari

AI-Native Engineer, Agentic Practitioner, Professor

How to Apply Agentic AI in Civil Engineering Practice

AI-Native Engineer, Agentic Practitioner, Educator

Identify how AI agents plan and execute multi-step tasks, and distinguish them from standard AI assistants.

Understand what agentic AI is

Analyze working examples across  engineering tasks to identify where AI agents reduce manual effort and project hours.

Map AI agents to real engineering tasks

Apply a prioritization framework to your own workflows and define one concrete implementation step.

Build a personal AI adoption roadmap

Become an Agentic AI Engineer by building real-world, production-ready AI agents that autonomously reason and execute complex tasks.

Cohort 2

Professor & AI Expert (540K+ Students)

Microsoft

General Motors

Samsung

HSBC

Dr. Ryan Ahmed

Professor, AI expert, & YouTuber who has taught 540,000+ learners worldwide.

Director of Engineering @Launchpad.AI

Nike

Levi's

Mr. Cooper

Launchpad

Kukesh Kodess

Director of Engineering with 7+ years building cutting-edge AI/ML systems

Cohort 1

Cohort 3

⭐"Join the instructor trusted by 540K+ students with 40K+ five-star reviews and 50M+ minutes of AI training watched worldwide."Agentic AI Engineering is 2026's most in-demand skill.Software is shifting from static systems to AI-native architectures, where autonomous agents reason, plan, and execute complex work.The problem: Most developers are stuck building API wrappers, and they lack the depth to manage state, memory, &amp; multi-agent coordination in real production systems.The Solution: This bootcamp is your bridge to elite Agentic AI engineering. We move beyond the basics to architect robust agent fleets in which specialized agents collaborate autonomously in production.What makes this course unique?<ul><li>Production-Grade Architecture: Engineer real systems using LangGraph, CrewAI, &amp; AutoGen.</li><li>Automate Agent Workflow Creation: Use Claude Code &amp; Codex to generate, refactor, and evolve agent workflows.</li><li>Enterprise Reliability: Design for guardrails, persistent memory, observability, &amp; human-in-the-loop safety. </li></ul><h3>Real-world Capstone Project</h3>Design &amp; deploy a production-grade, portfolio-ready agentic AI system for real-world use, demonstrating senior-level Agentic AI engineering skills.

Master Agentic AI Engineering & Architect Production-Grade Autonomous Systems

Architect, build & deploy production-grade agentic AI systems end-to-end

Orchestrate Autonomous Multi-Agent Teams

Master Tool Use & The Model Context Protocol (MCP)

Automate Workflows with AI Coding Assistants

Engineer Context-Aware Agents with Retrieval Augmented Generation (RAG)

Deploy, Monitor & Benchmark for Enterprise

Software Engineers &amp; Developers wanting to master state-of-the-art Agentic AI frameworks and build autonomous, production-grade AI agents.

Technical Founders &amp; CTOs who want to architect scalable, fault-tolerant autonomous systems that serve as your product's core engine.

Modern ML &amp; Data Science Practitioners who want to evolve from static models to dynamic Agentic AI workflows that reason, plan, &amp; execute.

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 5

O'Reilly instructor|Pearson SME|ex-Cisco

Cisco

Claro Brasil

Pearson

O'Reilly

Carmelo Iaria

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

Cognizant BR Turma 1

Cohort 6

This course is what you need to stay relevant — and protect your career.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 repeatable method you can apply immediately to your projects.What alumni sayBuilders from AT&amp;T, HP, Morgan Stanley, T-Mobile, Cognizant, Experian have applied the method I teach to build amazing multi-agent applications.<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>FINAL WEEK to Enroll to Cohort 5 (Enrollment closes Sunday May 31)

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.

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

Modernizing Business Analysis

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

Business Analysts, Product Owners, Business Systems Analysts, Systems Analysts, Business Process Analysts, and similar roles.

BA Leaders redefining roles for AI-enabled teams

Business Leaders, Project and Product Professionals pulled into AI initiatives without clear accountability

By the end of this course, you’ll transition from a software engineer to a confident AI engineer—able to identify the right problems, design

AI Engineer & Consultant for F-500 firms

Qualcomm

Wipro

Sujeet Mishra

AI Engineer | Entrepreneur | AI Consultant & Trainer for Fortune-500

AI/ML | Search | Ex-Microsoft, Yahoo

Apple

Yahoo

Prasenjit Mukherjee

Adjunct Faculty | Quantum ML & Big Data | Ex-Microsoft, Yahoo | IIT Madras

For software engineers, moving into AI engineering is not just about learning new tools—it’s a mindset shift.Over the past year, we’ve worked with engineering teams, from Fortune 500 companies to unicorn startups, helping them adopt AI-native and agent-based systems. One pattern shows up repeatedly: engineers don’t struggle because they can’t build, but because AI systems behave very differently from deterministic software.⚙️ Engineers must learn how to design, evaluate, and operate probabilistic systems, reason about failures and trade-offs, and think end-to-end—from use-case identification to production and scale.Many engineers hesitate because they believe AI engineering requires deep ML or statistics expertise. In reality, modern AI engineering is about system design, evaluation, observability, and production readiness—skills engineers already have but need to apply in a new context.🚀 This course provides a practical 6-week roadmap to make that transition by building and extending a real, production-grade agentic system.By the end, you won’t just understand AI systems—you’ll think and operate like an AI engineer.For queries, write to us: ai-education@theseuslab.ai

A 6-week roadmap for software engineers to become production-ready AI engineers

Use LLMs to 10x day-to-day engineering productivity

Design and build agentic systems using LangChain/LangGraph

Build reliable agentic systems by designing for evaluation and observability

Take AI ideas from use-case discovery to production and scale

Apply everything you learn to a real, end-to-end capstone project

Basic understanding of SDLC concepts like databases, APIs, frontend/backend systems, and servers

Familiarity with Software Engineering Fundamentals

Some programming experience, even if not recent—or willingness to spend ~8 hours pre-course to learn Python (we will provide resources)

Prior Programming Exposure (or Willingness to Ramp Up)

Openness to learning, experimenting, and thinking differently

Curiosity and Willingness to Experiment

Software engineers looking to transition into AI engineering roles and build production-grade AI systems

AI engineers and data science professionals who want to get a hands-on experience of building, evaluating and scaling agentic AI applications

Product managers and Tech leaders seeking a practical, system-level view of how production-ready AI applications are designed and shipped

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.