Connecting Dots
AI & Cloud Engineering for Tech Leaders
I write about technology, AI, cloud-native engineering, leadership, productivity, and the patterns I notice while building, mentoring, and learning in public.
Series
AI Token Economics
A practitioner series on LLM token pricing, per-turn optimization, and grounding strategies — what you're actually paying for, how to spend it well, and when grounding reduces costs versus drives them up.
Read the series →The Enterprise AI Playbook
A practitioner-led series on transforming enterprise systems into AI-native operations — covering data architecture, agent design, governance, LLMOps, and what AI-native actually means.
Read the series →Recent posts
Does Grounding Reduce Token Usage? It Depends.
Does grounding reduce LLM token usage? Three scenarios, three outcomes — and a self-evolving RAG knowledge base you can build in 15 minutes.
Five Ways to Spend Your Tokens Like They Cost Something
Five LLM token optimization strategies with failure modes: prompt compression, structured prompting, output control, and when each approach breaks down.
You're Not Buying AI. You're Buying Tokens.
LLM token pricing explained: understand what you're actually paying for in AI inference, how the token meter works, and when AI cost optimization matters.
AI Eliminates One Kind of Monotony — and Quietly Introduces Another
AI homogenization is the quiet risk in every AI productivity win. Same tools, same outputs, same strategy. Here's what actually becomes scarce.
AI Without Guardrails: 5 Failure Modes Every Team Hits First
AI guardrails aren't restrictions — they're reliability engineering. Learn the 5 failure modes and 5 control layers every AI deployment needs.
AI Guardrails in Action: 4 Experiments You Can Run
See AI guardrails in action: 4 before/after experiments with real system prompts, plus the open-source production tools engineers actually use.
When AI Does the Homework: What Happens to Human Creativity?
Generative AI in education isn't just a cheating problem — it's a thinking problem. Here's what's actually at stake for student learning.
What AI-Native Actually Looks Like — A Working Definition
What AI-native architecture actually looks like: five operational characteristics that define a binary boundary — you've crossed it or you haven't.
10 Agents in Production: Spec-Driven Development at Scale
Scaling AI agents in production with spec-driven development: stop asking 'does each agent work?' and start asking 'does the system still do what I designed?'
Prompt Injection, MCP, and the Trust Boundary Problem
MCP standardizes tool connections — and attack surfaces. Enterprise prompt injection via MCP starts with retrieved data. Trust boundaries as code, not policy.
Cloud, On-Prem, or Edge: How to Choose for AI Workloads
Cost is the wrong frame for AI workload deployment. Three variables actually decide: data gravity, inference latency, and compliance surface area.
LLMOps in Practice: Observability That Doesn't Lie to You
LLMOps observability patterns for production AI: four instrumentation approaches that tell you if the system is right — not just whether it's running.
Why Monolithic Agents Fail: The Case for Multi-Agent Design
Single agent vs multi-agent architecture in enterprise systems: the monolith fails the same way it always did — just faster, because agent errors compound.
CI/CD for AI Agents: 5 Pipeline Gaps to Close Before Launch
Testing non-deterministic AI agents in CI/CD exposes five gaps your pipeline wasn't built for. Here's what to close before agents reach production.
Building an AI Strategy That Governance Doesn't Kill
Speed versus governance isn't a tension to manage — it's an architecture problem to solve. Introducing the AI Bill of Materials and multi-model strategy.
The AI Debt You're Accumulating Every Sprint You Wait
Deferring AI readiness creates three compounding liabilities — technical, organizational, and competitive — that most deferral arguments never account for.
5 Questions That Reveal Whether Your System Is Ready for AI
A diagnostic framework for architects — five structural questions that surface where AI will accelerate your system and where it will collapse it.
The Cognitive Debt Crisis in AI-Augmented Codebases
Technical debt is future work owed. Cognitive debt is current comprehension, already lost — and it doesn't show up in your linter or coverage report.
Why Enterprise AI Projects Keep Failing — It's Not the Models
Most enterprise AI initiatives stall not because the model is bad, but because the data it needs was never designed to be asked questions. Here's why.
The Enterprise AI Playbook: From Legacy Systems to AI-Native
A practitioner-led series on transforming enterprise systems into AI-native operations — from diagnosing what's broken to defining what done looks like.