Deterministic Code in the Loop
Large scale Cloud migrations play the foil for my first DCITL use case. How does DCITL intersect with agent harness?
I published my first full framework for applying Deterministic Code in the Loop.
The use case is cloud migration, but the pattern is bigger than migration.
For the past year, the industry has talked a lot about human-in-the-loop AI. That framing is useful, but it does not scale as the primary control model for enterprise automation.
A human cannot inspect every recommendation, every code change, every migration step, every policy decision, and every exception across a large application estate.
So the question becomes: what sits between probabilistic AI and human review?
My answer is deterministic code in the loop.
In this framework, LLMs do not own the migration process. They are called only when developer-like adaptation is required. The control plane owns the process.
Playbooks constrain the work. Deterministic code executes known transformations. Agent harnesses provide controlled execution. Validators define done. The landing zone validates fit. Traceability records authority. Humans handle exceptions and authority boundaries.
That distinction matters.
Human-in-the-loop is not wrong. It is just insufficient by itself.
At enterprise scale, humans should not be the first line of defense against every AI mistake. They should sit at the authority boundaries: low-confidence decisions, exceptions, policy conflicts, failed validators, and material risk.
Everything else should be constrained, tested, validated, and recorded by deterministic systems.
Cloud migration makes the pattern visible because the risk is obvious. You cannot simply let an agent assess, plan, refactor, validate, and migrate workloads without knowing where decision authority lives.
That is why I frame the problem as a migration control plane, not an AI migration factory.
The core loop:
LLM proposes.
Playbooks constrain.
Deterministic code enforces.
Agent harness executes within bounds.
Landing zone validates.
Traceability records authority.
Humans resolve exceptions.
This is the practical model for using AI in enterprise automation without letting decision authority drift into the model.
Read the framework here:
https://thectoadvisor.com/blog/2026/06/04/from-migration-factory-to-migration-control-plane-2/