Perspectives on AI agents, automation systems, and building businesses that run without you.
AI adoption delivers business value only when systems are reliable under real-world conditions. Ignoring software resilience, especially in complex AI automation, leads to costly downtime and erodes trust.
Many AI projects stumble not because of technology but due to overlooked foundation work. Here’s a grounded framework to diagnose and fix common AI implementation traps, drawn from real-world operator lessons in 2026.
Service businesses can now improve AI agent efficiency and reduce operational costs by leveraging persistent agent memory and model compression—two advances reshaping margin dynamics in 2026.
Most AI agent guides start with "pick a model." That’s backwards. The model is the last thing you should choose — after the job, the judgment, and the workflow it needs to own.
Most automation fails because it tries to replace a human’s judgment instead of handling the parts of work that don’t actually need it. Here’s the pattern that works.
An AI agent that works in a demo but fails in production usually isn’t missing better prompts. It’s missing the three trust systems that make it safe to run without supervision.
Most companies measure automation ROI wrong. They count time saved instead of outcomes improved. Here’s a better framework.
The most useful AI agents aren’t the ones that can do anything. They’re the ones that do one thing extremely well — within the specific context, constraints, and language of a particular domain.