Module 1
Agentic AI and multi-agent systems
Move from single prompts to orchestrated systems with tools, memory, decision logic, and production reliability concerns.
Builder Track
AI engineering programs for builders moving from prototypes to production systems
A production-focused AI engineering program for builders shipping agentic systems, GenAI workflows, industrial AI use cases, and evaluation pipelines that survive real deployment.
Audience
AI engineers, product engineers, solution teams, technical builders
Format
Cohorts, labs, capstones, system reviews
Built for specific operators, not for a generic AI audience
Every track is scoped around a role, a decision context, and the kind of work participants need to leave ready for.
Grounded in NexaStack, ElixirData, and Akira AI
Focuses on delivery discipline, not demo-ready prototype theatre
Includes labs, reviews, and capstone execution
Fits both individual builders and enterprise engineering cohorts
What participants actually work through
Module 2
GenAI engineering and evaluation
Work on retrieval, grounding, evaluation loops, red-teaming, and failure analysis with production expectations in view.
Module 3
Industrial AI and domain execution
Explore digital twins, operational AI, and deployment patterns in manufacturing, energy, and infrastructure environments.
Module 4
Production capstone delivery
Ship a system, defend the architecture, and leave with work that looks more like deployment than coursework.
You're a fit if...
Builders who know the basics and want production depth
Engineering teams turning AI pilots into repeatable systems
Cohorts that want live labs and execution pressure, not passive content
This is not for you if...
Learners looking for an introductory no-code survey course
Teams that only want conceptual overviews without technical execution
Cohorts unwilling to commit to reviews, labs, and capstone discipline
How the track runs in practice
The outcome is not just attendance. Each track is designed to change decision quality, engineering capability, or institutional readiness.
Lab-first AI engineering delivery
Most of the learning happens through active build work, reviews, debugging, and implementation choices.
Production-grade standards
Participants learn against operating constraints, not idealized toy examples or sandbox-only demos.
Capstone review process
Each cohort is pushed toward something concrete: architecture, evaluation logic, system behavior, and deployment viability.
What this track is meant to change
Outcome 1
A stronger engineering approach to agentic and GenAI system delivery
Outcome 2
Capstone work that survives architecture and operations scrutiny
Outcome 3
Greater confidence in evaluations, red-teaming, and production readiness
Practitioner faculty across leadership, build, and governance
Faculty Director
Dr. Jagreet Kaur Gill
AI and Decision Intelligence · Agentic Enterprise Strategy
Leads the academic direction of XDFE with a focus on translating enterprise AI ambition into operating models, decision systems, and production strategy.
Builder Track Lead
Suryakant Tomar
Agentic AI · GenAI Engineering · Multi-Agent Systems
Works at the engineering edge of applied AI systems, helping builders move from experimentation into dependable agentic and multi-agent delivery.
Architect Track Lead
Chandan Gaur
AI Governance · EU AI Act · Sovereign AI Policy
Guides governance, standards, and sovereign AI thinking for institutions that need rigorous strategy, compliance alignment, and long-term operating control.
Leadership Track Lead
Riya Khurana
Applied AI Literacy · Adaptive Leadership · Responsible AI
Helps senior leaders build AI fluency, responsible deployment judgment, and the confidence to lead transformation without relying on abstract theory.
Explore the adjacent pathways in the academy
If builder track is the right fit, start the conversation now
We can route you into the right cohort, advisor workflow, or admissions path based on your role, region, and goals.