Mastering software architecture, technology leadership, and engineering excellence. The Architect View Master approach helps you understand systems from every viewpoint: business, technical, operational, and strategic.
Insights and perspectives on solution and software architecture
MCP fixes the biggest lie in enterprise AI: that your language model actually knows your data. By providing a standardized client-server protocol for connecting AI systems to real data sources and tools, it eliminates the hallucination problem at its root, while introducing new architectural and security challenges that architects can't afford to ignore.
The senior developer leaned back in her chair, watching the new hire struggle with the same AI prompt she'd seen three other teammates wrestle with that week. "Try asking it to include error handling and logging," she called across the room. The junior dev nodded, retyped the prompt, and got better results. But tomorrow, someone else would hit the same wall. Again.
Discover why reusing existing solutions before buying commercial tools and building custom ones can save your team time, money, and resources.
A senior architect at a healthcare tech company was presenting their new microservices platform to the executive team. Fifteen minutes in, the CTO interrupted: "Wait, so where does this actually run? Are we talking containers? VMs? Which cloud regions?" The architect pulled up what they thought was their deployment diagram: A tangled mess of boxes, lines, and AWS icons that looked like it had been designed by a caffeinated spider. The CTO squinted at the screen, then at the architect, then back at the screen. "I’ll just... ask the DevOps team later."
The solution architect leaned back in her chair, coffee in hand, reviewing the morning’s pull requests when Slack exploded. The security team had detected AWS credentials in the company’s public GitHub repository: credentials with full admin access to production. The commit had been live for exactly 47 minutes, just long enough for automated scanners to find them and spin up $12,000 worth of cryptocurrency mining instances. The developer who committed them? A senior engineer with eight years of experience, who’d been pair programming with an AI assistant and simply didn’t notice when it helpfully included the credentials from a local config file.
It was 2:47 AM when Sarah, a principal architect at a rapidly scaling SaaS company, finally admitted defeat. She’d been debugging for six hours, trying to untangle a "quick refactoring" that had somehow metastasized into 847 lines of changed code across 23 files. The tests were failing in mysterious ways, her Git diff looked like a Jackson Pollock painting, and she couldn’t remember which of her "improvements" had actually broken things. Sound familiar?
It started with one of the tasks on our project kanban board: "Data migration". We were building a new application for a telecom provider. The goal was to unify two old systems into a single custom-built platform. Development was already in motion. Sprints were delivering new features. But no one had touched the data. The legacy systems had mismatched schemas, overlapping records, and different definitions for the same business terms. Some orders were in one system, but not the other. Customer records had conflicting states. Worse, every new feature we built had to be fed by clean, compatible data. Any mismatch would break logic or trigger errors in production.
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