Agent Orchestration Is a Graph Problem, Not a Prompt Problem
Why linear agent chains waste most of their wall-clock, how to find the parallelism hiding in your workflow, and what actually breaks when you fan out to a fleet.
Blog
Writing about distributed systems, cloud architecture, AI/ML infrastructure, and engineering.
Views expressed here are my own and not those of my employer.
Your Agent Loop Doesn't Know When to Stop
An agent can always produce another answer. Whether the loop converges or burns money depends on four things, and none of them live inside the model.
The Forward Deployed Engineer Is Not a Solutions Architect in a Hoodie
The fastest-growing role in AI, reverse-engineered from the inside: what a Forward Deployed Engineer actually does, what the interview really tests, and why the model was always the easy part.
Google's ADK Is a Runtime, Not a Graph: Notes From Eleven Agents
Eleven agents in, the framework choice that mattered wasn't ergonomics or graph syntax. It was whether the runtime had opinions about events, state, and transport. ADK does. Here is what that buys you in production.
Built to Be Cited: An Engineer's Guide to AEO and GEO in 2026
An implementation-grade walkthrough of AI-search optimization in 2026, centered on Google AI Mode and Gemini, with the schema graph, llms.txt work, IndexNow wiring, and the debugging sessions that shipped the result — drawn from rebuilding my own site.
Agentic Coding Is a Different Skill
The executive summary of a free, open-source playbook for Claude Code and Gemini CLI: the handful of ideas that earned their place after months of trial and error.
Agentic Mesh, or Just Another Mesh? Auditing the Lineage Argument
The mesh metaphor for agent systems inherits a lineage that's rewriting itself. A careful audit of where the load-bearing work actually is.
Inference's Techniques Age in Years. Its Economics Age in Weeks.
What survives and what inverts in modern inference engineering: the physics holds, but the build-vs-buy math shifts every quarter.
Unlocking the Secret Sauce of LLMs: Why the Math Matters - A Technical Deep Dive
Exploring the essential mathematical foundations that practitioners need when working with Large Language Models, from linear algebra to optimization.
Beyond Frontier Models: A Technical Deep Dive & Practitioner's Guide to LLM Tuning, Optimization, Deployment, and Architecture
A comprehensive guide covering post-training techniques, production deployment, retrieval augmentation, evaluation, hallucination management, and advanced architectures for LLMs.