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8 changes: 8 additions & 0 deletions docs/src/content/docs/reference/glossary.md
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Operational patterns (suffixed with "-Ops") are established workflow architectures for common automation scenarios. Each pattern addresses specific use cases with recommended triggers, tools, and safe outputs.

### BatchOps

Pattern for processing large volumes of work items efficiently using chunked pagination, matrix fan-out, or rate-limit-aware sub-batching. BatchOps splits a backlog into parallel or sequential chunks, handles partial failures with `fail-fast: false`, and aggregates results into a consolidated report. Use when items are independent and order doesn't matter. See [BatchOps](/gh-aw/patterns/batch-ops/).

### CentralRepoOps

A [MultiRepoOps](#multirepoops) deployment variant where a single private repository acts as a control plane for coordinating large-scale operations across many repositories. Enables consistent rollouts, policy updates, and centralized tracking using cross-repository safe outputs and secure authentication. See [CentralRepoOps](/gh-aw/patterns/central-repo-ops/).
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Testing and validation pattern executing workflows in isolated trial repositories before production deployment. Creates temporary private repositories where workflows run safely, capturing safe outputs without modifying your actual codebase. See [TrialOps](/gh-aw/patterns/trial-ops/).

### WorkQueueOps

Pattern for incrementally processing a backlog of work items using a durable queue backend — issue checklists, sub-issues, [cache-memory](#cache-memory), or GitHub Discussions. Each run picks up where the last left off, making it resilient to interruptions and rate limits. Items should be idempotent and independently processable. See [WorkQueueOps](/gh-aw/patterns/workqueue-ops/).

## Related Resources

For detailed documentation on specific topics, see:
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