Preventing Agent Failures in Enterprise AI: Flaky Demos to Production Workhorses
Dheeraj Saxena
Principal Consultant. MD @ Datawhistl
Your AI agent works in demos. But Production isn't one.
📉 Most production deployments underperform or fail — Gartner expects over 40% of agentic AI projects to be cancelled by 2027. 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 AWS's Well-Architected Generative AI Lens stop at defining best practices but completely lack the specificity required to prevent production failures.
⚖️ Governance standards like AIGP 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.
What you’ll learn
From flaky sandbox demos to consistent business value. Proven, structured techniques to architect agentic AI systems in production.
What they cover, and how to apply them to real agent planning, design reviews, and engineering.
3 real examples of Agentic AI failures cutting across multi-channel retail, property/life insurance, and auto industries.
Understand why the failures happened, and how principles could have prevented them.
Concrete specifications of principles aligned to AWS GenAI lens best practices, but equally valid for Azure and GCP deployments.
ARB-led gated reviews, plus pre-commit hooks, CI/CD gates, and AI-based code scanners to identify violations of principles.
A maturity model that buckets the principles into 3 tiers — low-hanging fruit, scale-up, and cutting edge — so you can sequence adoption.
Workshop agenda
How Production AI Agents Actually Fail: Three Industry Postmortems
Forensic walkthroughs across retail, insurance, and automotive — what each system did, what the architecture missed, and why the model was never the problem.
AI Architecture Principles: Why We Need Them?
An introduction to AI architecture principles as a distinct discipline — why we need them.
Principles Catalog - Build your own or align to an existing framework?
Build your own — spend 18 months reinventing or align to one cloud. Review a catalogue anchored to AWS GenAI Lens but written platform-agnostic, so it ships on AWS, Azure, GCP, or self-hosted.
Six Principles, Built Live: From Lens to Production-Ready Spec
Hands-on sessions: build 4-6 principles cutting across various stages of the AI project lifecycle, identify the problems they solve, and design their enforcement.
The Implementation Toolkit-How to operationalise principles in your company
The Architecture Review Board process for Agentic AI systems, plus automated enforcement scripts — pre-commit hooks, CI/CD gates, and code scanners — that catch principle violations before they ship.
Learn directly from Dheeraj
Dheeraj Saxena
Ex-IBM, TCS, Wipro Consultant. | 25+ Years Scaling Data, AI & MarTech Solutions
Who this workshop is for
Senior Developers who are responsible for building production-grade Agentic AI workflows.
AI Governance & 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.
What's included
Live sessions
Learn directly from Dheeraj Saxena in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
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