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Gravixar

Writing

AI tooling

Building with AI agents in production: the confidence floor, the exception handling, and the parts that take considerably longer than the demo.

  1. Human in the loop AI: three live examples, and how to tell if the human is real

    Three systems I run put a person between the AI and the outcome. I check each one the same way: can the work move without them, and can I count the times they said no.

  2. Parallel AI review finds too much, and the volume is the failure mode

    Fourteen agent lanes over one codebase returned 63 findings in an afternoon. The count is not the result. What made the list usable was the pass that tried to kill every finding, and the two calls where the obvious action turned out to be wrong.

  3. The bottleneck is approval, not generation

    This site runs an AI agent that drafts blog posts twice a week. Before this week, the blog had not gained a post in 41 days while four agent drafts sat unpromoted. That queue is the design, not the failure.

  4. The workflow that reviews and delivers an AI image is worth more than the image

    Midjourney and Firefly now hand you commercial-licensed images for under $50 a month. The generation is free. The approval trail, versioning, and delivery governance still cost real money, and that is where the margin lives.

  5. Fast AI builds break in front of clients. Here is what to build instead.

    Every AI-ops shop is selling speed. Live in two weeks. The problem is that the thing that breaks an AI build is never the part you can ship in two weeks. It is the exception nobody designed for.

Bring me a real operations problem. I will show you the system running before you sign anything.

The Ops Leak Audit is the fixed-price first step: I count the hours your tools cost your team, then price the fix. Rather talk first? Book a 30-minute call. If it is not a fit, you still leave with notes you can use.

Start with the auditBook a call
Portrait of Qamar
Qamar, the person you would work with