Planning

Anchors facilitation workshops to NIST's voluntary Artificial Intelligence Risk Management Framework (AI RMF 1.0, formally NIST.AI.100-1 with DOI https://doi.org/10.6028/NIST.AI.100-1): the playbook issued alongside the Framework emphasizes structuring programs around the mutually reinforcing core functions GOVERN → MAP → MEASURE → MANAGE rather than improvising unrelated security tickets. NIST contemporaneously publishes companion assets such as the Trustworthy AI Resource Center playbook (airc.nist.gov), roadmap, crosswalks, and—for generative workloads—the Generative Artificial Intelligence Profile (NIST AI 600-1, July 26, 2024, DOI https://doi.org/10.6028/NIST.AI.600-1)—so teams can reconcile novel failure modes against documented categories of trustworthiness. This operational skill folds those authoritative layers into scripted prompts for cross-functional councils that must evidence documentation, escalation paths, quantitative trustworthiness analyses, prioritized mitigations, and alignment with externally referenced stakeholder expectations—not marketing slides.

Designs clean handoff protocols between specialized agents so work passes between planner, coder, reviewer, and executor agents without losing context, creating circular dependencies, or introducing race conditions. Handoff design treats agent-to-agent communication as an API contract with versioning, error handling, and explicit acknowledgment requirements.

Structures multi-step agent tasks with explicit inputs, outputs, fallback behavior, and handoff protocols so agents reliably complete complex workflows instead of stopping at the first blocker. Agentic workflow design applies software engineering discipline to AI agent pipelines, treating each step as a function with typed inputs and outputs.