The workflow aims to assist applied data scientists in designing GenAI agent prototypes by producing an agent architecture blueprint, tool schemas, prompt scaffolding, evaluation plans, and failure-mode analyses. The system must convert a high-level task description into a structured, testable, and cost-aware prototype design. The output must be systematic, engineering-focused, and ready for iteration and versioning.

The goal is to help a Head of Information Systems convert initiative details into a structured, enterprise‑grade delivery plan. The workflow must translate high‑level program inputs into actionable scope, milestones, RAID items, RACI assignments, communications planning, and operational readiness artifacts. This ensures predictable delivery, vendor alignment, and clear accountability across teams.

The goal is to take a list of data engineering tasks and transform it into a production-ready orchestration design. This includes defining a clear DAG blueprint, mapping explicit dependencies, and incorporating reliability mechanisms such as retries, backfills, logging, metrics, and cost controls. The workflow should help a Data Engineering Lead produce consistent, idempotent, and well-documented orchestration designs.