Why AI Coding Agents Hallucinate Architecture
AI coding agents hallucinate architecture when prompts lack locked decisions, validation gates, and a durable execution boundary.
The failure mode
AI coding agents hallucinate architecture when they are asked to infer system boundaries from conversation alone. The reliable fix is not a longer prompt. It is a governed workflow: locked decisions, validated outputs, and isolated execution.
Most AI coding failures start before code is written. A model receives a task, fills in missing architecture, and produces files that look plausible. The generated code may compile, but the system boundary has already drifted.
Prompting alone cannot lock a system
A prompt is a request. An architecture document is a constraint. The two are not interchangeable. If the workflow depends on the model remembering every decision, the workflow will fail under long sessions, handoffs, and repeated edits.
Logic64 treats architecture as input data, not context decoration. The generator works from a defined stack, a routing model, and validation rules that decide whether an output can become a bundle.
The governance pattern
A governed AI coding workflow needs three controls. Decisions must be explicit before generation starts. Generated artifacts must be checked against those decisions. Execution must be isolated so one request cannot pollute the next.
That is the difference between raw prompting and governed generation. Raw prompting asks an AI to be careful. Governed generation gives it rails and verifies that it stayed on them.