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Multi-agent production pipelines are orchestrated by decomposing goals into sub-tasks and assigning them to specialized agents with RACI roles, guardrails, and budget configurations to ensure safe, scalable execution.

RACI delegation in multi-agent coordination assigns Responsible, Accountable, Consulted, and Informed roles to specialized agents, enabling dynamic crew formation and Pub/Sub delegation for robust, self-healing operations.

ML deployments are configured by creating a DIO instance in config mode, where you define infrastructure profiles, select provider models, and establish budgets to drive controlled, production-grade orchestration.

Yes, data pipeline governance supports auto, config, and hybrid modes, each applying guardrails and spec-driven contracts to enforce compliance across specialized agents during full lifecycle orchestration.

Limitations of multi-agent orchestration include the need for explicit infrastructure profiles and budget configurations to prevent runaway costs, as dynamic crew formation requires strict guardrails for safe production execution.

To start multi-agent orchestration, create a DIO instance in config mode with infrastructure and budget settings, then run the orchestration command to execute a full task plan across specialized agents.