sadd:do-in-steps

Multi-step LLM orchestration coordinates complex tasks by decomposing objectives into sequential subtasks, dispatching specialized sub-agents, and verifying outputs with independent judge agents. Context passes concisely between steps to maintain continuity without leaking unnecessary details.

Judge-based verification uses independent judge agents to validate each sequential AI subtask output against clear pass/fail criteria before proceeding. This enforces workflow reliability through controlled execution and limited retry loops triggered automatically upon step failures.

Task decomposition breaks complex objectives into ordered subtasks with clear inputs and outputs. This approach enables sub-agent orchestration by dispatching focused agents with scoped context, models chosen per subtask, and controlled data flow for robust automation.

Yes, sub-agent orchestration supports model selection chosen per subtask. You can dispatch focused agents with scoped context and controlled data flow, allowing specific LLMs to handle individual steps within a single end-to-end workflow.

Context-aware progression passes concise, relevant context between sequential workflow steps to maintain continuity. This controlled data flow ensures sub-agents receive necessary inputs for independent verification without leaking unnecessary details across the orchestration pipeline.

When a sub-agent fails independent judge verification, the workflow enforces error handling via limited retry loops. This mechanism ensures workflow reliability by re-attempting failed steps under clear pass/fail criteria before proceeding to subsequent sequential subtasks.