Ai on A Field CTO Perspective

Inside the AI Harness: The Substrate Around the Chat

Tue, 13 Oct 2026 00:00:00 +0000

Written by a practitioner who has spent three years building AI apps, harnesses and code development tools. See About the author at the end.

Why the harness matters

In 2026, what an AI agent can actually do depends less on the model and more on the software wrapped around it. That wrapper is the harness.

A language model on its own is a function: text in, text out. It cannot read a file, run a test, remember yesterday, or check its own work. Everything that lets it do those things lives outside the weights.

The Rules Nobody Wrote Down

Tue, 29 Sep 2026 00:00:00 +0000

For twenty years, good software depended on people remembering which parts of a program were allowed to talk to which. AI coding tools don’t remember. So the rule has to be written down where a machine can enforce it.

A growing share of new code is written by AI. One peer-reviewed study estimates that by early 2025 about 29 percent of new Python functions pushed to GitHub from the United States were AI-written, the highest rate the study measured, up from about 5 percent in 2022.

The deterministic half of neuro-symbolic AI already exists. It's called network verification.

Sun, 26 Jul 2026 00:00:00 +0000

Everybody’s suddenly got a take on neuro-symbolic AI. Inference for pattern recognition, symbolic logic paired in for reasoning you can actually trust, and the EU AI Act’s enforcement phase lighting a fire under all of it because “the model said so” stopped being an acceptable audit trail.

The pitch is real. Inference is powerful and unreliable, a statistical guess dressed up as an answer. Deterministic, mathematical models are reliable and narrow. Combining them, having the deterministic side check inference’s work, is turning into the actual path to AI you can trust in production. What’s less talked about: in networking, the deterministic half of that equation isn’t something you’d have to build. It already exists, and it’s been hardening in production for 7 years.

Delivering APIs for AI

Thu, 09 Jul 2026 00:00:00 +0000

The rise of AI agents and autonomous systems is fundamentally changing how we design and deliver APIs, shifting from human-centric interfaces to AI-native integration patterns that require new architectural thinking.

The Inflection Point

Technology innovation has repeatedly transformed how we build systems. From the fourth-generation programming languages (4GL) of the 1990s that abstracted database operations to today’s AI-powered code generation, each wave has forced us to rethink fundamental assumptions. We now stand at another such inflection point: the transition from human-interface (HI) APIs to agent-interface (AI) APIs.