Parallelization Patterns

To coordinate parallel agents, you apply standardized parallelization patterns with defined timeouts, retry policies, and consensus thresholds to manage execution and ensure reliable results.

Standard multi-agent orchestration patterns define roles for read-only, analysis, execution, and orchestration workflows, applying timeout tiers and dependency rules to coordinate sub-agents safely.

You handle timeouts and retries by enforcing consistent timeout tiers and retry policies across parallel agent groups, preventing stalls and recovering from transient failures during execution.

Yes, you can run read-only, analysis, and state-modifying agents in parallel by grouping them into parallel_groups with dependency rules and agent_weights to preserve correctness.

The best way to establish consensus is to apply formal consensus thresholds and agent weights, ensuring coordinated results when multiple agents execute tasks simultaneously.

Parallel multi-agent workflows stall or fail when lacking defined timeout tiers and retry policies; applying formal dependency rules and orchestration patterns prevents these stalls.