Loop Library

See the blueprint for the AI agent.

Every library example turns a familiar business process into an agent operating specification: objective, signal, context, reasoning, tools, authority, evidence, verification, and learning.

Concrete agent opportunities

These illustrative examples show how Atlas Loop finds the bounded outcome an agent can own, what the agent must understand and do, where people retain authority, and how the result is verified.

001Finance & accounting

Accounting cycle

See the operating specification for an accounting agent that can carry a transaction through capture, policy, approval, posting, reconciliation, reporting, and close.

CaptureRecordReconcileAdjustReportClose
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002Commercial operations

Lead to cash to renewal

See how a commercial agent could carry customer context from qualified lead through approval, fulfillment, cash, reconciliation, and renewal.

LeadApproveFulfillInvoiceCollectRenew
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003Customer operations

Issue to resolution

See how a resolution agent could preserve customer context through detection, diagnosis, action, verification, closure, and prevention.

DetectClassifyDiagnoseResolveVerifyLearn
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004Construction operations

Survey to completion

See how a project-delivery agent could carry a custom home from agreement and survey through design, procurement, permitting, construction, inspection, completion, and learning.

SellSurveyEngineerBuildVerifyLearn
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Start with one loop

Which loop should become your first AI agent?

Choose a recurring outcome where context gets lost, judgment repeats, and the same work must be verified every time.

Map Your First Loop