multi-agent

Parallel AI agents accelerate large-scale codebase audits by dividing work among leader and worker roles. A leader delegates narrow subtasks to workers, collects structured results, and synthesizes a unified report for faster execution.

Leader-worker orchestration synthesizes multiple agents by assigning a leader to delegate independent subtasks to workers with isolated contexts. The leader then aggregates structured outputs into a unified, synthesized report.

You need multi-agent orchestration when executing large-scale reviews, multi-file analyses, or audits where independent subtasks can run concurrently. It enforces clear role-based delegation to synthesize results faster.

Yes, parallel execution applies to multi-file analyses where independent subtasks run concurrently. The leader-worker architecture fans out the workload, aggregates structured subagent outputs, and synthesizes a unified report.

Handling failures during parallel AI agent execution relies on stopReason semantics to manage errors and termination. This ensures the leader aggregation process maintains isolated subagent contexts while synthesizing results.

Isolating subagent contexts during parallel task synthesis prevents interference between independent subtasks. This ensures workers execute narrow directives cleanly, allowing the leader to aggregate structured outputs without contamination.