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What should be deterministic versus model-assisted in an agent workflow?

Make structure, scope checks, and validation deterministic; use models for interpretation, implementation, and human-supervised judgment where ambiguity remains.

Direct answer

Use deterministic tooling for repeatable facts: artifact structure, readiness, scoped paths, validation commands, and evidence presence. Use coding agents for implementation and analysis within that structure, and reserve human judgment for ambiguous intent, risk, and acceptance decisions.

Diagram distinguishing deterministic CLI, model-assisted work, and human judgment.

Practical guidance

Make the next review decision easier.

Automate repeatable checks

A rule that can be stated precisely should be executable. This reduces review effort and avoids asking a model to remember policy details that tooling can enforce.

Use models where reasoning helps

Agents are useful for exploration, code changes, and synthesis, but their results should remain bounded by deterministic constraints and observable validation.

Keep accountable decisions human

Product tradeoffs, exceptions, security risk, and conflicting evidence require an explicit accountable decision, even when an agent provides analysis.

Verified demo evidence

A public CLI result, not a completion claim.

This command and result are from the website’s checked-in synthetic repository demo. Substitute your own repository paths and validation command when you apply the workflow.
$ day-shift init
Created .day-shift workspace
Next: run day-shift doctor

Authorship and sources

Trace this guidance to maintained product evidence.

Maintainer
Tianna McCoy ↗Day Shift maintainer; responsible for the repository-native workflow and release evidence referenced here.
Last updated
Tested Day Shift
v0.1.24

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Plain-language explanation of the Human-Agent-Contract: explicit intent, scope, validation, evidence, and reconciliation for coding-agent work.

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