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ARuntime Reference

AI Runtime Use Cases

Reference use cases for knowledge assistants, coding agents, support actions, scientific workflows, local assistants, browser AI, and edge systems.

Audience: Technical readers Reading time: 1 minute Status: Production guidance Last reviewed:

Use cases connect runtime layers to concrete tasks, risks, contracts, failure modes, evidence, and success metrics. They are reference patterns, not claims about a shipped ARuntime product.

Key takeaways

  • Add runtime controls in proportion to duration, state, side effects, data sensitivity, and consequence.
  • Keep the domain system of record outside model memory.
  • Use deterministic software when the workflow is stable and completely specified.

Use-case explorer

[ar_use_cases]

When runtime control is justified

Runtime controls become valuable when work spans several steps, crosses systems or tenants, invokes tools, changes state, pauses for people, needs recovery, or must be reviewed. The architecture should identify the minimal controls needed rather than importing every agent feature.

When to use a simpler system

Choose a simpler implementation when possible
Situation Prefer
One read-only generation with no sensitive data or durable state Direct model API with validation and basic telemetry
Fully specified transformation Deterministic code or codemod
Known business rules and forms Traditional application workflow
Search and retrieval without synthesis Search UI and source links
Fixed scheduled integration ETL/job scheduler

Maintenance record

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