Agentic AI in Enterprise: Why Infrastructure Readiness Matters More Than Model Capability

After 20+ years in enterprise architecture, I’ve seen that infrastructure readiness matters more than model capability for agentic AI deployment. Gartner predicts 40% of projects will be cancelled by 2027 due to infrastructure gaps, not AI failures.

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2025 in Review: The Infrastructure Readiness Lesson

2025 taught enterprise technology leaders a critical lesson: infrastructure readiness matters more than model capability. This year-end review explores platform engineering, data governance, healthcare AI breakthroughs, and five predictions for 2026.

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Serverless Showdown: Cloud Run vs Cloud Functions vs App Engine – Choosing the Right GCP Compute Platform

Serverless Showdown: Cloud Run vs Cloud Functions vs App Engine Choosing the Right GCP Compute Platform for Your Workload I’ve deployed applications to all three GCP serverless platforms—Cloud Run, Cloud Functions, and App Engine. Each has strengths, but choosing wrong costs time and money. I’ve seen teams spend weeks migrating from App Engine to Cloud […]

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Building Interoperable Healthcare Data Systems for AI: A Complete Guide to FHIR, Standards, and Governance

Healthcare AI fails when data remains siloed. This article explores how FHIR, SNOMED CT, and platform thinking enable interoperable healthcare data systems for AI at scale, with insights from EU, UK, and Ireland initiatives.

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Orchestrating Chaos: Why AWS Step Functions Became My Secret Weapon for Building Resilient Distributed Systems

Three years ago, I inherited a distributed system that processed insurance claims across twelve microservices. The orchestration logic lived in a tangled web of message queues, retry handlers, and compensating transactions scattered across multiple codebases. When something failed—and in distributed systems, something always fails—debugging meant correlating logs across a dozen services while the business waited […]

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Production Model Deployment Patterns: From REST APIs to Kubernetes Orchestration in Python

After deploying hundreds of ML models to production across startups and enterprises, I’ve learned that model deployment is where most AI projects fail. Not because the models don’t work—but because teams underestimate the engineering complexity of serving predictions reliably at scale. This article shares production-tested deployment patterns from REST APIs to Kubernetes orchestration. 1. The […]

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