Author
Jonathan Gardner
Cloud architect and technology leader with 20+ years of experience delivering enterprise solutions. Principal TPM, specializing in Azure, Kubernetes, and modern app development.
23 essays
Written by23 filed
Where Do I Even Start? (The Wrong First Question)
"Where do I even start with AI?" is the wrong first question. It skips a step that determines whether everything after it works or wastes your time. The right sequence: understand the technology, pilot with measurement, then evaluate which processes should look entirely different.
Your AI Agent Shouldn't Have Your Employee's Job Description
When teams "agentify" an existing process, they hand the agent a workflow designed around human constraints. The agent inherits every stop-and-wait point, every workaround, every approval layer it doesn't need. The fix isn't to optimize the agent. It's to redesign the process.
What I Learned Building a Document Format from Scratch
I started thinking the hard problem was document parsing. Then format design. Then sync logic. The real challenge was building at the interface between two domains that don't talk to each other.
The Design Decisions Behind an AI-Native Document Format
Processing a Word document through raw OOXML costs 359,706 prompt tokens. Clean markdown costs 106. Five design decisions that make that gap useful without losing formatting.
Why AI Document Workflows Are Broken
Every AI tool that works with documents treats formatting as disposable. For organizations running hundreds of documents through AI editing pipelines, it's a structural cost nobody is tracking.
Goal Translation Infrastructure: Encoding What Your Organization Actually Wants
Your Capability Map says a workflow is Agent Ready. Now what does the agent optimize for? OKRs were designed for humans. Agent Actionable Objectives are the translation layer that's been missing.
The Organizational Capability Map: Deciding What Your AI Agents Should Actually Own
Most AI deployments fail not because the technology doesn't work, but because nobody decided which workflows agents should actually own. The Organizational Capability Map is a framework for making that call.
The Intent Gap: Why Your AI Agents Are Optimizing for the Wrong Thing
Klarna's AI agent saved $60M then backfired. The problem wasn't the AI. It was the gap between what agents optimize for and what organizations actually need.
Multi-Agent AI Is a Distributed Systems Problem: Plan Accordingly
Microsoft announced agents talking to agents at Ignite 2025. What they didn't address: the distributed systems complexity that creates. If you've built microservices architectures, this will feel familiar. If you haven't, you're about to learn some hard lessons.
Agent 365: Plan for 18 Months, Not 3
Microsoft's Agent 365 demos compress 12-18 months of work into 10-minute presentations. The technology is real. The implementation gap is too. Here's what data readiness, integration complexity, and organizational change actually look like for enterprises planning AI agent deployments.
The Shadow AI Problem That Agent 365 Actually Solves
Enterprises are about to repeat every mistake they made during cloud adoption, but with AI agents. Here's what I learned from ten years of Azure migrations about why Shadow IT happens and what Microsoft's Agent 365 actually solves.
I Built a Claude Skill That Creates Claude Skills (Using Claude)
I used Claude Code to build a tool that creates Claude Skills. Open-source CLI tools for validating and packaging skills at scale. Built by AI, for AI, used by engineering teams. Includes practical examples for SDLC automation.
When Measuring Impact Gets in the Way of Actually Having Impact
Teams hit every metric—green dashboards, perfect scores. But customers were frustrated and critical incidents kept happening. Here's why: people spent more time proving they had impact than actually having it. Measurement systems don't just fail, they twist the work they measure.
The Only Three Questions Your TPM Dashboard Should Answer
Most TPM dashboards track the wrong things. Build success rates and sprint velocity tell you nothing about what matters. Every metric should answer one of three questions: Can we ship safely? Are teams fast enough? Will customers succeed? Stop measuring everything. Start measuring what matters.
Why Technical Debt Isn't a Technical Problem
Southwest Airlines lost $825 million in ten days because of a crew scheduling system from the 1990s. Pilots had warned management for years. This wasn't a technical failure—it was a leadership failure. Technical debt isn't a technical problem. It's a business problem disguised as an IT issue.
Stop Waiting for Legacy Systems: How the Strangler Pattern Fixes Real Integration Problems
A manufacturer's web store took 12 seconds per order because of synchronous SAP calls. Customers abandoned carts. We fixed this with the strangler pattern - not by making SAP faster, but by stopping the wait for SAP. Real code examples included.
Three Questions Before Any Technology Decision
A company spent $20M on software that didn't work. They picked tools first, then tried to solve problems. After 20 years at Microsoft, I learned to ask three questions before any tech decision. Start with problems, not solutions.
Moving from Engineer to Architect: The Skills That Matter
The shift from engineer to architect isn't about learning new tools. It's expanding your scope from local fixes to global impact. Engineers make code work. Architects think about what happens when that code scales, fails, or succeeds beyond expectations.
When Kubernetes Isn't the Answer: Right-Sizing Your Cloud Architecture
Teams pick Kubernetes before they understand their problem. This creates unnecessary complexity. I've seen simple apps that would work perfectly on Azure App Service struggle with container orchestration nightmares. Requirements should drive platform choice, not trends.
The Most Common Mistakes in Cloud Migration Projects
One bad call killed 2.5M connected cars. 75% of cloud migrations fail because teams assume apps work the same in cloud. They don't. Here are the 10 costliest mistakes and how to dodge them.
Making LLMs Work with Your Existing Data Systems: A Technical Leader's Guide to AI Integration
Learn proven strategies for integrating LLMs with enterprise data systems. Practical insights on architecture patterns, data pipelines, and security from hands-on experience.
Building a Custom Action Item Tool: Beyond Microsoft Teams Copilot's Limitations
Teams Copilot struggles with long meetings, 30-day history limits, and inconsistent formatting. Learn how to build a custom Azure solution using durable functions and OpenAI that processes any meeting length with unlimited history and team-specific templates.