Preventing Agent Failures in Enterprise AI: Marketing Personalization Case Study
Dheeraj Saxena
Principal Consultant. MD @ Datawhistl
Your AI agent works in demos. But Production isn't one.
Most enterprise AI agent demos work. Most production deployments underperform or fail outright. The developer brilliance that thrives inside sandbox demos delivered using Claude Code or Cursor evaporates 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.
Despite the pace of advances in models and tooling, no framework exists to guide how that architecture should actually be built. Governance standards like AIGP are simply too abstract to be actionable.
The Workshop USP
Using an anonymized UK retailer's case study, you'll learn how to 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.
You'll also leave with practical ways to operationalise these principles in your own enterprise using governance controls, architecture wrappers, and automated enforcement.
Workshop agenda
The $5.3M Friday: How Production AI Agents Actually Fail
A forensic walkthrough of a real production incident at a UK retailer — what the 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 generic SDLC principles miss the most expensive failure modes, and how the six map to all four AIGP domains.
Context & State Lifecycle Management (Principle 1)
How to version, refresh, and govern every piece of context your agent depends on — before stale embeddings hold data your customers have legally asked to be erased.
Enterprise Integration Patterns (Principle 2)
Circuit breakers, idempotency, graceful degradation, and the dedicated integration layer your agent needs from day one — before it outruns Salesforce and breaks the entire tenant.
Production-Realistic Testing (Principle 3)
Adversarial test generation, production-data parity, and failure injection that surfaces real risk before customers do — not after 94% test coverage misses the only case that mattered.
Security & Access Control for Agents (Principle 4)
Why agents need their own security model — not a user's, not a service account's. Granular permissions, full audit trail of every action, and a kill switch the engineering team doesn't control.
Evidence-Based Decisioning (Principle 5)
How to build reasoning traces that audit themselves — readable by regulators without engineering involvement, and how to avoid the 11-day forensic exercise the ICO put Hawthorne through.
Independent Evaluation & Multi-Model Architecture (Principle 6)
Self-assessment bias, multi-model orchestration, and why a model cannot judge itself — and what happens when evaluator and generator share a training corpus for too long.
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.
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.
Recognise prominent failure patterns of most enterprise AI today: duplicated mistakes, exponential remediation, stalled programmes and more.
The Architecture Review Board process for AI systems, gated reviews, and what to check.
Pre-commit hooks, CI/CD gates, and AI-based code scanners that catch principle violations before they reach production.
Learn directly from Dheeraj
Dheeraj Saxena
Ex-IBM, TCS, Wipro Consultant. | 25+ Years Scaling Data, AI & MarTech Solutions
Who this workshop is for
Senior Architects at large companies, who are responsible for architecting 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.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Frequently asked questions
$850
USD