Ai-Harnesses on AI For Human Expertise

Constrained Generation Is Symbolic AI Smuggled Into LLMs

Mon, 06 Jul 2026 00:00:00 +0000

The Toolkit Most Practitioners Already Reach For

Anyone who has shipped a language-model application beyond the prototype stage has, at some point, used constrained generation. The shape of the encounter is consistent. The model produces text that is almost right and occasionally wrong in ways the consumer cannot tolerate. Someone on the team reaches for one of a small set of tools: Outlines, lm-format-enforcer, the JSON-schema mode in their model API, a regex post-filter, a structured-output decoder. The wrong answers stop. The system ships.

A Five-Role Diagnostic For Any AI Agent

Mon, 22 Jun 2026 00:00:00 +0000

Most Agent Problems Have A Diagnostic

When an AI agent fails in production, the conversation almost always turns to the model. The team waits for a bigger model, a better-tuned variant, a different vendor, a new context window. Sometimes the team turns to prompts instead: more careful instructions, more examples, a sharper system message. Most failures are neither. They are failures of structure, and the structure question is a thirty-year-old framework most engineers have never read.

The Harness Is Where Symbolic AI Returned

Mon, 15 Jun 2026 00:00:00 +0000

The Standard Story About AI Agents Misses Half The System

The dominant story about building AI agents is straightforward: take a language model, prompt it well, give it tools, and let it loop. The model is doing the work. Everything else is plumbing.

This story is incomplete in a way that has practical consequences. The plumbing is not plumbing. It is a body of design decisions that determines whether the agent works in production, fails silently, or quietly degrades over time. And some of it, the parts that earn the name, is doing what symbolic AI has always tried to do: making reasoning explicit, checkable, and tied to a model of the world.