Thinking about AI infrastructure
Identity, governance, observability, and what it takes to ship autonomous software at scale.
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How an AI-Native Engineering Team Does Code Review
When the writer and the reviewer are the same intelligence, the pull request gate stops doing what it was designed to do. We rebuilt code review at AgentField from the ground up — what changed, why, and what we open-sourced.
The Hidden Primitive Behind Claude Code, Codex, and Gemini
Part 2 of the harness orchestration series. Four properties of the boundary surface of a single harness: the workspace it reads at startup, the boundary that drifts while it runs, the verifiers it can and cannot see, and the blast radius the team can afford to undo.
An Engineer's Guide to Harness Orchestration
Why orchestration changes when the unit decides for itself. The harness is a fundamentally new kind of computational object, defined by agency, embodiment, and persistence in one primitive. Part 1: The Black Box.
What is harness orchestration?
What changes when the atomic unit of intelligence is no longer an API call, or no longer a single LLM call. From invoking one model to orchestrating teams of autonomous harnesses like Claude Code, Cursor, Codex, Gemini, and the systems built above them.
Beyond Vibe Coding: How We Ship Production Code with 200 Autonomous Agents
What we learned orchestrating 200+ Claude Code instances on a shared codebase: two LLM primitives, three nested failure loops, and checkpoint-based execution.
What Breaks When AI Makes a Trillion Decisions
The world makes hundreds of billions of API calls every day. Each call carries a decision. Most of these decisions are hardcoded into configuration files and compiled binaries. But that's starting to change, and the infrastructure isn't ready.
A Useful Way to Think About Where AI Fits in Software
The speed at which agent-style systems have moved from research to daily use has been remarkable, even as their impact inside real business environments remains uneven. A clearer split begins to appear when you look at where intelligence sits in the architecture.
The AI Agent Accountability Gap
Why AI backends need tooling we haven't built yet. When a returns agent approved a $12,000 refund it shouldn't have, nothing looked wrong. The logs showed no errors, every validation check passed. The problem was that no one could explain why it made that call.
The Move from Monolithic AI to Modular Systems
What a documentation chatbot taught us about building AI features that scale. When web applications hit complexity, we extracted microservices. The same evolution is happening with AI.
IAM for AI Backends
How DIDs and Verifiable Credentials enable trust for AI agents
The AI Backend
Five years from now, every serious software company will have an AI backend - a reasoning layer that sits alongside their services, making decisions that used to be hardcoded.