Governing AI Agents Without Killing Them: What Actually Works in Production
Most AI agent governance advice targets boards, not builders. Three failure patterns, real TypeScript examples, and what a CTO should do Monday morning.
Production AI Engagements
I'm Damian Galarza. I work with engineering teams on retainer to ship reliable AI features, harden the AI stack, and make agentic systems that survive past the demo.
15+ years building production software. Former CTO at a regulated medical-device shop. Current senior engineer at a clinical software company. The patterns I bring are tested with real users, real token budgets, and real error rates — not adapted from blog posts.
15+
years building production software
0 → 50+
engineers scaled as CTO
FDA
cleared medical device software shipped
1+ yr
daily Claude Code user, documented publicly
$12,000 · 2 weeks
A focused 2-week engagement that produces a written architectural assessment and one shipped foundation piece. Either side can walk away after, or convert to retainer.
$15k–$60k/month
Ongoing engagement at the intensity you need — Advisory, Embedded, or Full. Flat monthly rate, flexible hours, direction reset every two weeks. Two-month minimum after Foundation Sprint, then 30-day notice.
Backed by a 30-day money-back guarantee. Also available: 1:1 coaching ($300/hr) for individual engineers and leaders working through a specific challenge.
"He quickly understood where we were with AI tooling and gave us immediately actionable advice, not generic frameworks. He identified gaps we hadn't considered, walked us through how he architects agent loops in production, and helped us think through our product-level agent strategy without over-engineering it."
"I found his videos especially clear-headed. I booked a couple of private sessions to discuss OpenClaw, and to my delight, he was equally clear-headed in person."
Most AI agent governance advice targets boards, not builders. Three failure patterns, real TypeScript examples, and what a CTO should do Monday morning.
How I added ElevenLabs TTS audio narration to my Hugo blog, cloned my own voice, and discovered my writing had patterns no voice model could read.
How I used autoresearch to run 65 autonomous prompt optimization iterations on a production LLM agent, cutting it 28% while retaining 98% output quality.
AI agents produce better output when the codebase is ready for them. Here are the four dimensions of codebase readiness that account for most of the gap.
Claude Code runs terminal commands and asks you to approve them. This explains what those commands mean and when to pause before saying yes.
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30 minutes. No pitch deck. We'll talk through what you're working on and I'll tell you honestly whether I can help.
No pressure, no upsell. Just a conversation to see if there's fit.
Insights on AI integration, engineering leadership, and building production systems. Written from ongoing practice, not past experience.
Occasional emails. No fluff.