Tata Consultancy Services

Wipro

Serco Group

Dheeraj Saxena

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

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Principal Consultant. MD @ Datawhistl

Who is this guide for

What is inside this guide

What You Will Walk Away With

More information about the product including FREE calculators

Model the real CDP cost before MTU spikes, burst traffic, and engineering delays hit production.

Founder and Chief Data Officer @ Datawhistl.com

How to Evaluate CDP Pricing and Cost Scalability: Packaged vs Warehouse-Native (Data.C1)

Who Is This For

What Is Inside

Key Benefits & Outcomes

More information about the product

Detailed guide on how to assess the cost of migrating between customer data platforms

CDP Data Ownership & Portability Toolkit: Migration Cost Calculator + Vendor Questions (Data.C2)

Why you need to a high-level discovery

Discuss your CDP requirements and get a fixed price estimate for due diligence.

Customer Data Platform Architecture Discovery

Your AI agent works in demos. But Production isn't one.

📉 Most production deployments underperform or fail — <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027">Gartner expects over 40% of agentic AI projects to be cancelled by 2027</a>. The velocity that Claude Code and Cursor give developers in the sandbox quietly hides architectural debt that production exposes the moment the agent meets real customers, real data, and real regulators. The cause is rarely the LLM call — it's the workflow architecture around it.🏛️ Frameworks like <a target="_blank" rel="noopener noreferrer nofollow" href="https://docs.aws.amazon.com/wellarchitected/latest/generative-ai-lens/generative-ai-lens.html">AWS's Well-Architected Generative AI Lens</a> stop at defining best practices but completely lack the specificity required to prevent production failures.⚖️ Governance standards like <a target="_blank" rel="noopener noreferrer nofollow" href="https://iapp.org/certify/aigp">AIGP</a> focus on control rather than engineering patterns and lack practical actionability for the engineering team.This workshop closes that gap.Using real case studies, you'll learn how to identify and apply AI architecture principles to design agents that survive contact with reality — real customers, real data, and real regulators. And do so in a repeatable, auditable manner across your entire Agentic AI project portfolio.

From flaky sandbox demos to consistent business value. Proven, structured techniques to architect agentic AI systems in production.

What AI architecture principles are

What happens if you do not use architecture principles

Alignment with AWS Well-architected-framework/GenAI Lens

How to enforce principles through governance and CI/CD pipelines

Step-by-step framework to help you decide which principles you need

what they automate, what they decide, what's being deployed where. You don't need to code them; you do need to understand them.

Business understanding of AI agents and what they do in enterprises

Senior Developers who are responsible for building production-grade Agentic AI workflows.

AI Governance &amp; Risk Professionals translating governance policy into architecture decisions that engineers can actually build against.

Business Heads/Project Managers accountable for AI initiatives and who need to understand architecture challenges without getting into code.

Why Most Data Platform Failures Happen Before Build Starts

Chief Data Officer, and Enterprise Architect at Datawhistl.com

Understand the real cost of skipping Discovery — in rework, delays, and lost stakeholder confidence.

Why skipping Discovery and jumping to HLD breaks programmes

Learn techniques to identify red flags, validate requirements, and develop estimates during high-level discovery.

How to identify show-stoppers before the budget is committed

Real case study: how wrong Discovery questions set programmes up to fail — how architecture principles prevent this.

Learn how architecture principles save programmes

Why Enterprise AI Agents Fail Beyond The Demo

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

Understand how AI architecture principles replace sandbox heroics with specific, enforceable production controls.

Prevent AI agent sprawl at scale

Use the AWS GenAI Lens (Azure / GCP optional) as your universe — your catalogue makes each best practice enforceable.

How to use AWS GenAI Lens as the source of AI principles

Take a real failure apart and see which principle would have caught it before it ever shipped.

Walk through a real production failure

Both cause production failures — understanding the scope of each helps build concrete controls that prevent them.

Dheeraj Saxena Chief Data Officer | Scaling Enterprise Data & AI

Dheeraj Saxena

About Dheeraj

Workshops

Preventing Agent Failures in Enterprise AI: Flaky Demos to Production Workhorses

Lightning Lessons

Why Most Data Platform Failures Happen Before Build Starts

Why Enterprise AI Agents Fail Beyond The Demo

Services

Customer Data Platform Architecture Discovery

Resources

How to Evaluate CDP Pricing and Cost Scalability: Packaged vs Warehouse-Native (Data.C1)

CDP Data Ownership & Portability Toolkit: Migration Cost Calculator + Vendor Questions (Data.C2)