parallel-agent-orchestration

You can accelerate complex workflows by dispatching independent subagents concurrently and aggregating their outputs. This approach decomposes requests like account audits into independent subtasks, runs them in parallel, and merges the results into a single coherent output.

Concurrency control manages parallel execution by enforcing limits and shared resource constraints. This prevents rate limit issues and controls costs while multiple subagents process independent data sources or documents simultaneously during task execution.

Conflict resolution handles contradictory recommendations by applying confidence-based or rule-based strategies during result aggregation. This mechanism identifies conflicting findings from concurrent subagents and resolves them to produce one consistent set of recommendations.

Task decomposition is needed when facing tight wall-clock constraints and independent data sources. You split complex requests into truly independent subtasks like auditing multiple campaigns or reviewing many files, enabling concurrent execution rather than slow sequential processing.

Yes, parallel orchestration applies to batch code reviews by treating each file as an independent subtask. Subagents run concurrently across the files, and their findings are aggregated into a single set of recommendations with any conflicts explicitly resolved.

Yes, budget management enforces shared resource constraints during concurrent execution. This limits costs and manages rate limits while subagents process independent tasks in parallel, ensuring resource consumption stays within defined boundaries.