A conventional program gives the same output when it receives the same input and state. A language model may not. It can produce a strong answer, a weaker answer or a different approach on the next run. That variation is useful for interpretation, planning and diagnosis. It is also the reason a model should not quietly become the control system for high-stakes work.
Reasoning and authority are different jobs.
An agent may inspect a requirement and suggest a code change. It may compare three solutions, explain a failure or prepare a test plan. These are reasoning tasks, where probability helps. Merging code, accessing production, changing a payment rule or closing a security finding are authority decisions. They need explicit boundaries and repeatable evidence.
Confusing the two creates a dangerous shortcut: the agent sounds confident, so the system lets it proceed. Confidence is not proof. A convincing explanation can still sit beside a failing test, an unauthorised dependency or a broken latency budget.
The model proposes. The engineering system decides.
Put a deterministic harness around intelligence.
The harness should own permissions, approved tools, test thresholds, architecture rules, retry limits, budgets, audit and rollback. It decides whether an agent may read a repository, change a file, call an external service or promote a release. Its behaviour is visible and testable.
Inside that boundary, agents can use probabilistic reasoning freely for the work it does well. Outside it, nothing happens only because a model asked. Every material action passes a control that can return one of a few clear outcomes: proceed, correct the work, request approval or stop.
Evidence should beat self-assessment.
Asking an agent whether its own change is correct is useful as an early review, not as the final gate. The result must be tested by deterministic systems: executable BDD scenarios, compilers, static analysis, dependency checks, security scanners, performance thresholds and deployment policy.
These tools are not intelligent in the model sense. That is their strength. A test either passed under the defined conditions or it did not. An API contract changed or it did not. A response met the latency budget or it did not. Agents can interpret this evidence and plan a fix, but should not rewrite the standard to suit their output.
Autonomy should be earned by risk.
Not every change needs a person. A low-risk documentation correction can move automatically after its checks pass. A database migration or production access change should require stronger evidence and human approval. Autonomy should depend on the action, affected system, confidence and blast radius—not on a broad claim that the agent is autonomous.
This also supports bounded retries. If an agent fails a gate, it may diagnose and try again within set limits. Repeated failure, uncertainty or policy conflict brings in a person. The loop remains useful without becoming endless or unsafe.
TechMojo Software Factory separates agent reasoning from engineering control.
BDD contracts and architecture rules define the allowed work. Lead, Execution, Verification, QA and DevOps Agents collaborate inside that boundary. Four verification pillars produce machine evidence before promotion, while an autonomy policy decides when work can proceed and when a person must intervene. Bounded retries, circuit breakers and audit keep the process controlled. A model gateway adds swappability and routes each task to an appropriate model at an appropriate cost.
Explore TechMojo Software Factory →Model choice should not change the control model.
Models will improve, prices will change and specialised models will appear. A software factory should be able to route planning to one model, code understanding to another and a simple classification to a smaller, cheaper model. None of these changes should alter the permissions, evidence gates or audit trail.
This is an important test of the architecture. If replacing a model changes who can approve a release or what counts as a passed verification, too much authority has leaked into the probabilistic layer.
Controlled intelligence is more useful intelligence.
Deterministic control is sometimes described as a restriction on AI. In reality, it is what allows an enterprise to use agents for serious work. Teams can grant more autonomy when they know the boundaries are enforced and every important step leaves evidence.
The aim is not to remove probability from software. It is to place it where it adds judgement and keep control where the organisation can depend on it.
This field note is part of TechMojo's series on production-grade software engineering.