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Why Enterprise AI Agents Fail Beyond The Demo

Hosted by Dheeraj Saxena

Mon, Jun 22, 2026

11:00 AM UTC (1 hour)

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Preventing Agent Failures in Enterprise AI: Flaky Demos to Production Workhorses
Dheeraj Saxena
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What you'll learn

Prevent AI agent sprawl at scale

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

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

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

Walk through a real production failure

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

Understand how AI governance differs from AI architecture

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

Why this topic matters

Coding demo agents in a sandbox can be a thrilling hobby, but architecting them for production scale is a serious discipline. Without rigid principles, AI devolves into "development spaghetti"—fragile logic relying on individual heroics rather than systemic standards. Without these principles, most AI deployments remain shelfware that no serious, regulated environment would risk putting into

You'll learn from

Dheeraj Saxena

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

25+ years building enterprise data platforms and intelligent customer systems, with the last 5 years focused on Generative AI solutions and classic ML platforms. Former senior consultant and advisor at IBM, Tata Consultancy Services, and Wipro.

Specialising in Enterprise AI engineering and AI-ready data architecture — including RAG systems, Agentic workflows, customer data platforms, semantic modelling, AI governance, evaluation pipelines, and MLOps.

Grounded in real enterprise delivery across Retail, Insurance, E-commerce, and Automotive verticals.

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