multi-agent-action-system

name	multi-agent-action-system
description	Use when designing specialized Dataverse AI agents for sales, operations, compliance, data stewardship, ALM, or other enterprise action domains. Produces an operating model only and does not run agents.

Multi-Agent Action System

Purpose

Design a multi-agent operating model for Dataverse and Power Platform actions.

Use this skill to define:

• Sales Agent for quotes, forecasts, pipeline, and customer analysis
• Operations Agent for processes, tickets, and escalations
• Compliance Agent for audits, DLP, policies, and risk analysis
• Data Steward Agent for data quality, duplicates, ownership, and validation
• ALM Agent for deployments, solution checks, and dependency validation

V1 Boundary

This skill defines roles, responsibilities, handoffs, and governance only. It does not spawn or operate live agents.

Workflow

1. Identify the business domain and required specialist agents.
2. Assign each agent clear responsibilities, allowed decisions, required evidence, and escalation boundaries.
3. Define handoff contracts between agents: input, output, confidence, risks, and approvals.
4. Define shared memory and audit requirements while avoiding unauthorized cross-domain data exposure.
5. Define governance: owner, approval model, monitoring, failure handling, and human override.

Output Format

Return:

• Agent map: specialist agents and responsibilities.
• Handoff model: inputs, outputs, confidence, and escalation rules.
• Shared context: what can be shared and what must stay restricted.
• Governance model: approvals, audit, monitoring, and override.
• Implementation path: skills, flows, queues, and Dataverse structures to design.